Chiron-o1: Igniting Multimodal Large Language Models towards Generalizable Medical Reasoning via Mentor-Intern Collaborative Search
Authors: Haoran Sun
Yankai Jiang
Wenjie Lou
Yujie Zhang
Wenjie Li
Lilong Wang
Mianxin Liu
Lei Liu
Xiaosong Wang
Venue: NeurIPS 2025 poster ()
Keywords: Multimodal Large Language Models
Medical Reasoning
Chain-of-Thought
Primary area: applications
TL;DR: We present MICS, a multi-model collaborative search strategy that facilitates the generation of effective step-by-step CoT data.

Abstract: Multimodal large language models (MLLMs) have begun to demonstrate robust reasoning capabilities on general tasks, yet their application in the medical domain remains in its early stages. Constructing chain-of-thought (CoT) training data is essential for bolstering the reasoning abilities of medical MLLMs. However, existing approaches exhibit a deficiency in offering a comprehensive framework for searching and evaluating effective reasoning paths towards critical diagnosis. To address this challenge, we propose Mentor-Intern Collaborative Search (MICS), a novel reasoning-path searching scheme to generate rigorous and effective medical CoT data. MICS first leverages mentor models to initialize the reasoning, one step at a time, then prompts each intern model to continue the thinking along those initiated paths, and finally selects the optimal reasoning path according to the overall reasoning performance of multiple intern models. The reasoning performance is determined by an MICS-Score, which assesses the quality of generated reasoning paths. Eventually, we construct MMRP, a multi-task medical reasoning dataset with ranked difficulty, and Chiron-o1,  a new medical MLLM devised via a curriculum learning strategy, with robust visual question-answering and generalizable reasoning capabilities. Extensive experiments demonstrate that Chiron-o1, trained on our CoT dataset constructed using MICS, achieves state-of-the-art performance across a list of medical visual question answering and reasoning benchmarks. Codes are available at https://github.com/Yankai96/Chiron-o1

Chiron-ol: Igniting Multimodal Large Language Models towards Generalizable Medical Reasoning via Mentor-Intern Collaborative Search
Haoran Sun 12", Yankai Jiang"" Wenjie Lou', Yujie Zhang?
Wenjie Lits, Lilong Wang', Mianxin Liu', Lei Liu?, Xiaosong Wangl
'Shanghai Artificial Intelligence Laboratory
Fuudan University
Shanghai Jiao Tong University
j Llanyaanka@@jjall ong cn, vangziaooson@@pjabb. org.a.b..r....oom..... o
Abstract
Multimodal large language models (MLLMs) have begun to demonstrate robust reasoning capabilities on general tasks, yet their application in the medical domain remains its early stages. Constructing chain-of-thought (CoT) training data is essential for bolstering the reasoning abilities of medical MLLMs. However, existing approaches exhibit a deficiency in offering comprehensive framework for searching and evaluating effective reasoning paths towards critical diagnosis. To address this challenge, we propose Mentor-Inted Collaborative Search (MICS), a novel reasoning-path searching scheme to generate rigorous and efffective medical CoT data. MICS first leverages mentor models to initialize the reasoning, one step time, then prompts each intern model continue the thinking along those initiated paths, and finally selects the optimal reasoning path according the overall reasoning performance of multiple intern models. The reasoning performance is determined by an MICS-Score, which assesses the quality of generated reasoning paths. Eventually, construct MMRP, multi-task medical reasoning dataset with ranked difficulty, and Chiron-o1, a new medical MILMM devised via a curriculum learning strategy. with robust visual question-answerii answering and generalizable reasoning capabilities. Extensive experiments demonstrate that Chiron-ol, trained on our CoT dataset constructed using MICS, achieves state-of- the-art performance across list of medical visual question answering and reasoning benchmarks. Codes are available
Introduction
Multimodal Large Language Models (MLLMs) 8 23 26
72 have demonstrated prominent performance in a wide range of tasks, including image captioning. visual question answering, and video analysis. Recent breakthroughs in MILMs have also shed light the development of general-purpose medical AL. Remarkable efforts have been made to adapt existing MLLMs to address complex clinical tasks through supervised fine-tuning (SFT) on carefully curated multimodal medical instruction fine-tuning datasets [7] 28 30
49
65 69]. However, most current medical MLLMs rely on direct-prediction paradigm, which produces brief, immediate answers to problems and often overlooks the interleaved image-text reasoning processes essential for real-world clinical scenarios. This critical shortcoming, namely the inability to perform deep multimodal reasoning analysis of
'Equal contribution.
Corresponding authors.
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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medical data, has given rise to an urgent need for the development of more sophisticated medical MLLMs capable of tackling complex clinical challenges and offering improved diagnostic support.
Building on recent advancements in reinforcement learning (RL) optimization, several promising have leveraged Group Relative Policy Optimization (GRPO) 43 to elicit reasoning capabilities in models, introducing multiple RI-series medical MLLMs. Nevertheless, these models continue to exhibit limited performance in answering real-world clinical questions. As pointed out by recent studies [75]. although RL training improves performance by biasing the model's 's output distribution toward reward-yielding trajectories, fails to generate novel reasoning paradigms. Consequently, the performance ceiling of the RI models has long been constrained by their underlying base MLLM. One potential solution is to incorporate high-quality ch thought (CoT)) annotations into SFT to bolster the model's ' reasoning capabilities and foster novel reasoning paradigms. However, unlike general-domain tasks in which large-scale CoT datasets can be crowdsourced, medical reasoning demands domain-specific logical structures and clinical expertise. Curating such medical CoT datasets prohibitively expensive and time-consuming. Furthermore, the absence of standardized evaluation metrics to ensure the validity of generated CoT processes also remains a critical barrier to step-by-step visual reasoning annotation. Therefore, it is crucial to develop an effective and sophisticated method that can generate intermediate reasoning steps toward the final answer and evaluate the step-by-step visual reasoning quality.
approaches [24]] 39 47
Motivated by the aforementioned challenges, propose Mentor- Intern Collaborative Search (MICS), a new multi-model collaborative searching strategy designed generate effective step-by by-eepp CoT data. The core idea of MICS is leveraging multiple knowledgeable mentor models to collaboratively search for reasoning paths, while evaluating the searched paths based on feedback from intern models. The entire search process aligns with the logic of how a mentor guides interns, where the effectiveness of the mentor's 's guidance validated by whether interns can correctly solve problems based on the provided instructions, i.e., initialized reasoning paths. We integrate the valuable insights of multiple mentor models, retaining effective steps and discarding low-quality or hallucinated ones to identify the correct reasoning paths. Along the searching, we propose an MICS-score to enable the evaluation of CoT data quality, to further facilitate the efficient construction of multimodal reasoning data. Building the proposed methods, we develop MMRP, multimodal medical reasoning dataset comprising three subsets: simple question-anae answerr (QA) pairs, image-text aligument annotations, and MICS-generater multimodal CoT data for complex clinical scenarios. Thus, we employ a novel curriculum learning paradigm that progressively infuses medical knowledge, from fundamental concepts to complex cases in MMRP, thereby enhancing the model's 's reasoning capabilities. Finally, we achieve Chiron-ol, general-purper multimodal medical model with multimodal CoT reasoning capabilities through stae SFT with the composed curriculum.
We conduct a comprehensive evaluation on seven benchmarks, containing both in-domain and out-of-domain scenarios, rigorously assess the performance of Chiron-ol. The results indicate that Chiron-ol exhibits robust multimodal reasoning capabilities, outperforming the SOTA medical reasoning models across all benchmarks. Our contributions can be summarized as follows:
We present MICS, a multi- collaborative search strategy that facilitates the generation of effective step-by-step CoT data.
We construct MMRP, high-quality multimodal medical dataset comprising QA pairs, image-text alignment data, and effective reasoning paths for complex medical VQA prob- lems, spanning 12 imaging modalities and 20 body systems.
We develop Chiron-ol, a new multimodal medical model that demonstrates outstanding reasoning abilities in handling both in-domain and out-of oft-of-dmmiin complex clinical problems.
We demonstrate that our approach achieves competitive performance compared to previous SOTA medical MLLMs through extensive experiments across multiple benchmarks.
2
Related Works
Reasoning in Medical MLLMs. As multimodal reasoning demand grows, frameworks and tech-
niques have evolved, extending the reasoning capabilities of Large Language Models (LLMs) [18
51 52
to tackle more complex general-purpose vision-language tasks [54 72 9 3]. Techniques
such prompt tuning (76), SFT [44), and RL [[]] have emerged as critical methods for boosting the

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reasoning capabillities of MILMs. Given its potential to enhance model's performance on complex tasks, reasoning ability has also garnered significant attention in medical domains. Models like HuatuoGPT-ol 7 and Baichuan-M1 55 are dedicated to enhancing the ability to solve medical problems through rigorous and transparent reasoning paths. However, equipping MILMM with reasoning capabilities by integrating multimodal medical information remains an open and chal- lenging problem. Recent studies [35 64 have explored designing well-crafted prompts to simulate the reasoning process from medical problems to correct answers, often involving multiple doctor roles or functional modules. However, the applicability of the aforementioned methods is limited. Meanwhile, DeepSeek-R1 R1 significantly enhances model reasoning capabilities through GRPO [8]. an increasing number of studies follow this paradigm to adapt RL to the medical domain. Med-R1 1 [24), utilizing the GRPO strategy, demonstrates superior performance across different image modalities, enhancing the generalizability of MI.LMs in medical reasoning. Similarly, MedVLMMR
39 adopts RL framework encourage the discovery of human-interprettale reasoning paths. However, these RI--based models rely solely on fixed reward functions (e.g., format and accuracy), neglecting the evaluation of reasoning paths, which can easily lead to superficial or hallucinated reasoning processes. In contrast, our proposed method leverages collaborative search to identify high-quality reasoning paths with minimal hallucinations, thereby enhancing reasoning abilities.
(4)
Pathology
(b)
DSA
Ulrissund
Ned NM
PET/SPECT
Mammographh
MPI
X-ray
SLACE
VVC
Figure 1: Overview of the MMRP Dataset and Chiron-ol Performance. (a) The MMRP dataset encompasses 12 imaging modalities and 20 body systems. (b) Chiron-ol achieves SOTA performance across various benchmarks compared existing multimodal medical models.
Construction of CoT Datasets. The development of CoT datasets for SFT is crucial in multimodal
by emphasizing the reasoning process rather than merely providing final answers 661 63 Research in this field can be categorized into prompt-based, plan-based, and learning-based methods (62], each contributing uniquely to the CoT generation in multimodal contexts. Promet-bbsed methods utilize carefully designed prompts to guide models in generating CoT during inference. Simple prompts, such as "think step-by-step" [21], initiate creation of reasoning paths for multimodal tasks, while advanced approaches establish clear reasoning workflow by specifying detailed objectives and procedures for each step [10] [5] 37). Few-shot prompts often include reasoning examples 66 80), offering flexibility in resource-limite scenarios or when rapid responses are needed. Plan-based methods enable dynamic exploration of reasoning paths, enhancing adaptability. For example, MM-ToT [17 combines GPT-4 Diffusion 42), using depth- first and breadth- first searches to select optimal solutions. HoT 77) encapsulates interconnected thoughts within hyperedge structures, while AGoT [68] builds reasoning graphs integrating visual data. Additionally, learning-based methods embed CoT construction within model training fine-tuning. PCoT (56) optimizes this approach for CoT generation, whereas MC-CoT (48) improves reasoning capabilities in smaller-scale models via majority voting during training. However, as constructing medical CoT requires specific logic and domain expertise, the aforementioned methods cannot be effectively transferred to the medical field. In other words, the construction of multimodal CoT in the medical domain remains an underexplored stage. Existing studies (44) primarily rely on manual annotations or model-generatee reasoning paths without any evaluation. Our proposed method takes a step further by addressing these challenges by leveraging an automatic and collaborative search strategy to identify high- high-quality reasoning paths.
reasoning, setting it apart from traditional models [ 29
23 and Stable
3

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3 Methods
In this section, we elaborate on our method for eliciting effective reasoning capabilities in MLLMs to tackle complex clinical problems by constructing multimodal medical CoT datasets and applying SFT. Specifically, we propose a novel curriculum learning scheme tailored for training medical MLLMs. It involves leveraging the constructed dataset MMRP to progressively inject medical knowledge into MLLMs, advancing from foundational principles to complex problems. Concretely, we develop QA and image-text alignment datasets to strengthen the model's foundational capabilities in the medical domain. Subsequently, we introduce the MICS strategy to generate high-quality CoT data for complex medical scenarios. MICS also incorporates a novel evaluation metric designed assess the effectiveness of reasoning paths. Ultimately, using the aforementioned datasets, we perform stage-wise SFT to train our model, Chiron-ol, which demonstrates robust reasoning performance.
3.1
MMRP Dataset
Currently, numerous websites and publications provide complex clinical cases for educational purposes, such as Radiopaedia [T)] BMJ Case Reports [13], and World Journal of Clinical Cases
19 These platforms aim to enhance learners' knowledge and, more crucially, translate it into tangible improvements in individual competence for daily clinical practice. Utilizing the data released in [8] (mainly from Radiopaedia (16)). we additionally aim to recompose training data for robust reasoning capabilities. Specifically, we construct the MMRP dataset with three subsets. Part of the MMRP is designed to create a QA dataset at the level of clinical trainees. Initially, we collect over 60K+ cases containing pure text information, encompassing neurological disorders, cardiovascular abnormalities, skeletal system diseases, and more. Subsequently, QA pairs are synthesized in segmented manner based on the information richness of the cases. Furthermore, we compose Part 2 of the MMRP with aligned image-tex: pairs. Unlike other methods that generate synthetic image captions 77.. texts in this subset are entirely sourced from authentic medical imaging analyses in the educational site, covering various imaging findings such as masses and lesions, anatomical abnormalities, inflammatory changes, and more. Further details about Parts and 2 of MMRP are provided in the Appendix [ A.2 Next, construct multimodal CoT data in Part 3 of the MMRP using MICS to enhance the reasoning capabilities of MLLMS.
3.2 Mentor-Intedd Collaborative Search for Effective Reasoning
Besides the data in Parts 1 and 2, the MMRP dataset also aims to construct subset of multimodal reasoning paths by leveraging valuable clinical case information. To explore effective reasoning paths, we propose the MICS, multi-model collaborative reasoning path search strategy. The strategy emulates the mentor-intern dynamic, where effective guidance depends on interns' performance in correctly solving problems using the prompts. The core idea is to leverage powerful mentor models to iteratively search for and identify valuable reasoning steps, shown in Figure
2 The effectiveness
of these steps is determined by feedback from intern models. Inspired by CoT [53] 67] and existing
process reward models [57 59 79), we believe that models can address problems by thinking in
step-by-step manner, thereby deriving answers in a reasonable and understandable way. To evaluate the quality of each step, we define its value as the potential of leading to the correct answer.
We denote the mentor models {0I,..., 0n)) and the intern models {B1,..., 8., ). The former are vole-played with generalist models, while the latter typically is composed of open-sourced models, often much smaller in size. During searching, the step k an intermediate reasoning step generated by a 0, is represented as 8k,, accordingly. The searching begins at the <start point> and subsequently explores effective reasoning paths through iterative search. It involves three key operations.
1) Collaborative search for reasoning paths by multiple mentors The objective of this operation is to combine the knowledge acquired by multiple mentor models to collaboratively search for effective reasoning paths. This approach not only enhances the diversity of reasoning paths but also mitigates the risk of single model unilaterally solving a problem, particularly when the model's 's understanding of the problem is biased. Specifically, starting from the <start point> or the optimal path selected in operation 3), each mentor model @ generates complete solution, thereby deriving the correct answer. In practice, this process involves acting as a "completer", extending the reasoning path prefix. The entire process can be formalized as follows:
{ {81 $2,ys .... } MICS | (0....., On, (a)...,,)))
(I)

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Sk+1, ~ 8.( P.Q. fsi.e. $2.9. 1k,
(2)
where MICS; denotes the search process up to the step k. and {s1,z $2.y: ... Sk.z represents the all reasoning steps searched thus far, serving as the reasoning path prefix for the subsequent search. P. Q, A, and I represent patient information, questions, ground truth, and corresponding images.
<start points
Image
Dredicne
Question
Scores Ressoning Path Prefiies
stop
Slep
slep
MICS Sca-S
0,
COO
Po
Thinking
step
step
step
Step 2
Step 2
stop 2
step 2
stop
step
Scores
Otherwise
Any Scores
step 2
step 2
ktep
Step
step 3
step 3
step 3
step
step
C
step 1
select the highest
step 3
step 3
step
answerr
SnSwer
Snower
anower
step i
step
save effective reasoning path
stap K
Ground Truth
<start points
enter next
MICS Scoper
MECS Storet
MELS Snore 0.5
-and point-
Mentor
OO
Intern
step
new start point, search from step
Figure 2: Framework of the MICS Strategy. MICS enables search for effective reasoning paths through collaboration between mentor and intern models until the maximum search depth is reached or carly-stopping conditions are met. denotes the mentor model, and B denotes the intern model. The example of CoT construction using MICS is provided in the Figure 7
2) Evaluation of reasoning paths based on MICS-Score We argue that relying on closed-source MLLMs evaluate the steps a reasoning path arbitrary [7T]. Instead, the value of reasoning path explored by mentor model 0 should be assessed by intern models B. Fundamentally, this operation involves the intern models B completing the entire reasoning process based on the prompt (reasoning path prefix) provided by the mentor model 6. The effectiveness of the reasoning path prefix is then measured by comparing the answer decoded by the intern model against the ground truth. This validation logic aligns with the principle that "a mentor's ' guidance is effective only if interns can correctly solve problems based on the provided prompts. Specifically, we employ lightweight models from diverse families with varying temperatures as intern models /3 to improve sampling diversity and randomness. Ultimately, the quality of a reasoning path is defined as the frequency with which achieves the correct answer. The entire evaluation process is formalized as follows:
1 aw ~ (. QQ (81,2) 82.y Sk,z 8k+1,0), I),
w ranging from m
(3)
MICS-SCore(Sgeet)) number of LLM( A)
number of ب
where $ and ã denote the reasoning path completed by the intern model 3. and the final answer derived, respectively. Subsequently, we employ an LLM [31] to compare the generated answer with the ground truth to determine its correctness. The proportion of interns answering correctly is calculated as the value score (MICS-Score) of the reasoning path prefix.
3) Selection of the optimal reasoning path Following the previous operations, we obtain the reasoning prefixes generated by different mentor models in the current search iteration, along with their corresponding value scores. In each search iteration, we select the reasoning path with the highest score as the reasoning prefix for the next search, continuing until the maximum search depth reached. MICS strategy is not solely guided by high-value outcomes but also incorporates exploratory attributes
5.. If multiple reasoning path prefixes in a iteration yield the same scores, i... score( (f (1,,, $2.y..... Sk sk+1,e) equals score (($1,e) $2.g. ...: Sk,z, $k+1,r)). We prioritize the reasoning step generated by a mentor model f, that was not selected in previous iterations as the current step. Additionally, during the search process, we implement two "early stopping" mechanisms. First, if all mentor models in a search iteration reach a score of zero, the data is marked as search failure, triggering a subsequent re-search. Second, if the current reasoning path prefix enables all intern models to derive the correct answer, the search is terminated. Particularly, if multiple full-score
5

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reasoning paths exist, we calculate the competitiveness of the corresponding mentor models, as follows:
Competiivennes((Onn)) - II MICS-Score((s<ch (1,03)
(5)
where scy denotes the reasoning path searched up to the i-th iteration, and Competitiveness(4 (O,, n)
represents the competitiveness score of the mentor model at the 1++h)-th search, calculated by multiplying the value scores obtained in previous search iterations. A higher competitiveness score indicates that the mentor model possesses clearer insights into the problem compared to other models.
In summary, the MICS strategy leverages three key operations to iteratively search until the maximum depth is reached or full-score reasoning path is identified. By applying MICS to complex clinical problems, we can construct a set of high-quality, step-by-step reasoning triplets, comprising questions, reasoning paths, and answers, thereby enabling MLLMs to learn to reason.
3.3 Chiron-ol: Multi-Stage SFT for Reasoning Emergence
Inspired by curriculum learning 4 45 60]. we design three-stage model training strategy based on different task types. This strategy emulates the human learning process, progressing from simple to complex knowledge acquisition. Specifically, we begin by training the model pure text medical QA tasks, gradually enabling it to answer simple medical questions and provide concise explanations. Subsequently, the model is trained to become familiar with the characteristics of medical images, achieving effective image-text alignment through authentic clinical images and their analyses. Unlike methods that generate image findings from images and captions (7), utilize the original image analyses from cases as the supervision signal during this stage to minimize the introduction of fabricated information. Building on these stages, the final training stage leverages high-quality CoT data, generated by the MICS strategy in complex medical scenarios, to stimulate the model's reasoning capabilities. However, the MMRP dataset alone is insufficient to endow the model with comprehensive multimodal medical knowledge, which could hinder the emergence of reasoning abilities. Therefore, we incorporated several commonly-used Visual Question Answering (VQA) datasets 7 20 25 32] 788 to bolster the model's foundational capabilities. In practice, all curated datasets are mixed in specific proportions during the training process to train the model effectively.
Di - J[VA(VQ_SSel Σ MM))))art!)), rranging from to
(6)
L(2,1)= Σ
A
(QYYA))D
where f()) denotes the fusion of the utilized datasets in specific proportions, detailed in the Appendix C We iterate through all SFT data (consisting of triplets of questions, reasoning paths, and answers, like (Q, Y, A)) from the predefined dataset D; to train the MLLM Q
4 Experiments and Results
4.1 Experiment Settings
To evaluate our model, we utilize two types of medical benchmarks: VQA bench- marks and reasoning benchmarks. (1) The former focuses on testing the model's ' foundational capabilities, such as visual understanding. We utilized the VQA-RAD (25, SLAKE 33, Path VQA 20), PMC-VQA [78), and the Health & Medicine track subset of the MMMU dataset 74]. (2) The latter is designed to evaluate the model's 's reasoning capabilities when addressing complex problems. This benchmark includes MedXpertQA_MM _M (multimodal subset) 83) and the MMRP test set. MedX- pertQA_Mi MM is divided into two subsets emphasizing reasoning and understanding. respectively. The
Benchmarks
MMRP test set encompasses two pre-divided subsets of pure text and multimodal reasoning data.
Implementation Details In our experiments, the MICS strategy employs three mentor models: ChatGPT-40 23). Gemini 2.5 Pro Preview [50), and (wen2.5-VLL-72B-Inme [3]. To validate the searched reasoning paths, we designate three open-source models intern models, each queried

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under two temperatures (0.3 and 1.2): Owen2-VL-7B [58], Qwen2.5-VL-7B B and Intern VL3- 8B [9]. We employ DeepSeek- V3 [3] as the "judge" compare intern answers against the ground truth. To balance search efficiency and reliabillity, the maximum search depth is set to 4.
For model training, adopt IntemnVL3-8B the base model and apply LoRA fine-tuning 22 with AdamW as the optimizer, a learning rate of 4e-5 with cosine decay, a batch size of 16, and bfloat 6 mixed precision. Training is conducted eight NVIDIA A100 GPUs with DeepSpeed ZeRO-1 41 The three curriculum stages require approximately 12 hours, 12 hours, and 48 hours, respectively, totaling 72 GPU-hours. The training corpus consists of the MMRP dataset and several commonly used medical VQA datasets, with detailed composition provided in Appendix
Baseline Methods & Evaluation Metrics We compare Chiron-ol with the following three types of baseline models: (1) General MLLMs, including high-performance general vision models, both closed-source (GPT series [23), Gemini series (50)) and open-source (LLaVA series [33], Qwen series BD. (2) Medical MLLMs, comprising vision models pretrained on specific medical corpora, such as LLd 28 GMAI-VL 30 and HuatuoGPT- Vision 77. (3) Medical Reasoning MLLMs, including Med-RI [24] MedVLM-RI [39), and ChestX-Reasoner [14], which are fine-tuned using GRPO [43. For evaluation metries, we use choice accuracy to measure model performance on VQA benchmarks. For the open-ended questions requiring reasoning in Part 3 of the MMRP dataset, BERT-Score 77 is employed assess the semantic similarity between the model's final answer and the ground truth, while MICS-Score is used to evaluate the effectiveness of the reasoning path.
Table 1: Main Results on Medical VQA Benchmarks. Our model achieves SOTA performance across various benchmarks. Bold denotes the highest score, and underline denotes the second -ighess..
Methods
VQA-RAD
SLAKE
PathVQA
PMC-VQA
MMMUCH&M
AVG
Close-Souree Source SOTA
Gemini-1.5-Pro
49
60.3
72.6
70.3
52.3
47.9
60.7
Gemini-2.5-Pro
12
71.3
80.5
73.9
61.1
57.1
68.8
GPT-4o-mini
26
55.8
50.4
48.7
39.6
48.6
GTP-4o 23
54.2
50.1
59.2
40.8
52.1
Open-Source SOTA
LLAVA-v1.5-7B
33
54.2
59.4
54.1
36.4
38.2
48.5
L.LVA-v1.6-13B
55.8
58.9
36.6
48.5
LLaVA-v1.6-34B
44.4
Yi-VL-34B 73
53.0
58.9
47.3
39.5
41.5
48.1
Qwen-VL-Chat
47.0
56.0
55.1
36.6
32.7
45.5
Medical MLLM
LLaVA-Med 28
51.4
48.6
56.8
24.7
36.9
43.7
Med-Flamingo 38
45.4
43.5
54.7
23.3
28.3
39.1
RadFM [66]
50.6
34.6
38.7
25.9
27.0
35.4
GMAI-VL
66.3
72.9
54.3
PraatuooTT-Vision---7
63.8
74.5
59.9
52.7
49.1
HuatuoGPT Pe-innGGGTVViiiin---ee Visionnnnn
7
68.1
76.9
63.5
58.2
$4.4
64.2
Reasoning Medical Model
Med-R1
55.9
55.1
53.3
45.8
32.7
MedVLM-RI
61.4
65.9
55.2
44.8
35.5
52.6
70.9
70.0
66.7
38.5
49.5
59.1
Intern VL3-2B
68.3
65.9
65.2
49.1
38.4
57.4
Chiron-ol-2B
75.4
85.3
70.3
54.3
42.1
65.5
InternVL.3-8B
73.1
71.1
67.9
53.2
52.1
63.5
Chiron-ol-SB
76.8
83.2
74.0
57.5
$4.6
+5.2
4.2 Main Results
Performance on the Medical VQA Benchmarks Medical models should strive to further enhance their performance on VQA tasks while improving reasoning capabilities. Therefore, to evaluate foundational performance of Chiron-ol, we first test it on several commonly-used VQA benchmarks. The results, compared against other mainstream models, are summarized in Table Compared the baseline models, Chiron-ol achieves significant performance improvements across five benchmarks.

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Table 2: Results on Medical Reasoning Benchmarks. ACC represents accuracy, and MICS-Scon refers to the evaluation metric described in Equation
indicates pure text reasoning models.
MMRP (uure Text)
MedXpertQA, L_M MM
MedXpertQA MM
MMRP (Reasoning)
Model
(Reasoning)
(Understanding) ACC
ACC
ACC
ACC
Bert-Score MICS-Score
MedReason*
79.2
HuatuoGPT-o11*
85.1
Med-R1
72.7
20.1
20.8
28.1
83.4
22.5
MedVLM-RI
77.5
21.7
20.0
31.2
83.5
23.5
Chiron-ol-2B
90.6
19.8
23.1
43.8
88.2
32.2
Chiron-ol-SB
92.1
23.3
25.1
58.4
90.4
49.4
Specifically, Chiron-ol tatron-ol--B and Chiron-ol outperform their respective baseline models by an average of 5.7% and 8.1.. This demonstrates that our reasoning-focuser model further enhances visual understanding and guestion-answerts models that were neither pretrained nor fine-tuned on multimodal medical datasets. The results in Table findicate that our model easily surpasses these large-scale MLLMs. Subsequently, Chiron-ol outperforms most medical MLLMs across all benchmarks. It even achieves comparable performance to HuatuoGPT- Vision--s 33B,, which has four times the parameters, on the PMC-VQA and MMMU (H&M) benchmarks, while significantly surpassing it on the remaining benchmarks, with overall average improvement of 5%. Finally, we observe that existing medical reasoning models, such as Med-R1 and MedVLM-R1, tend to lose basic VQA capabilities while focusing reasoning, as shown in Table In contrast, Chiron-ol-88 outperforms them on VQA benchmarks by an average of 20.6% and 16.6%, respectively. Overall, Chiron-ol demonstrates competitive performance across benchmarks, highlighting its versatility in medical image understanding and question answering.
capabilities. Next, we compared Chiron-ol with SOTA
Performance Medical Reasoning Benchmarks We further evaluate the performance of Chiron- ol on reasoning benchmarks, including in-domain (MMRP) and oft-of-domain (MedXpertQA. MMM datasets. [4)] posits that training multimodal reasoning models may compromise their textual reasoning capabilities. Therefore, we first assess Chiron-ol on the pure text reasoning data of the MMRP dataset. The results in Table 2 demonstrate that model significantly outperforms the visual reasoning models Med-R1 and McdVLM-RI. Even compared to pure text reasoning models, Chiron-ol surpasses MedReason and HuatuoGPT-ol by 12.9% and 7%, respectively. These results strongly validate that our proposed MICS and stage-wise training strategy effectively enhance the model's text reasoning capabilities. In the multimodal reasoning domain, Chiron-ol excels in both the accuracy of final answers and the effectiveness of reasoning paths compared to other reasoning models. The results in Table indicate that Chiron-ol-8h consistently outperforms MedVLM-RI (the best among other reasoning models) by 27.2% in accuracy and 6.9% in semantic similarity. The effectiveness of reasoning paths is another key focus. Compared to all reasoning models, Chiron-ol- 8B achieves the highest score of 49.4% the MICS-Score. Our model not only excels on in-domain datasets but also demonstrates robust performance on oft-of-domain benchmarks. Chiron-ol outperforms Med-R1 and MedVLM-RI MedXperIQA MM by average of 3.75% and 3,35%, respectively. Figure 3 illustrass that Chiron-ol can engage in deep and reasonable reasoning for real-world complex clinical problems, ultimately providing correct answers.
4.3 Ablation Studies
Effect of MICS Strategy We apply the MICS strategy to build the MMRP multi-task dataset to improve the model's multimodal reasoning. An ablation study on Chiron-ol-8P evaluates its impact. Unlike MICS, the vanilla method enables the mentor model to generate reasoning directly, bypassing the evaluation of intern models. Table 3 shows that excluding reasoning data (first row) or using reasoning paths constructed by vanilla method(second and third rows) degrades performance. Compared to Chiron-ol-883 the former reduces average performance by 3% across four benchmarks, and the latter by 2.7%. Furthermore, Figure Jillusttaas the differences in effectiveness between reasoning paths constructed using MICS and the vanilla method across three distinct medical scenarios. We compute the MICS-Score step-by-ster for an entire reasoning path to evaluate the value of reasoning prefixes and analyze the trend of MICS-Score changes (categorized into four types, with monotonically increasing indicating effective trend and the others deemed low-value,
8

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user
Bused on the iraging findings of abrornal flattening of the laft parieto-cscipital hoves in this 20 zi--gaa-old vain patient, what is the most likely dagnosie for this incidental finding? Please base your response on the koyframes of the relevint medical inaging modulit (Modility cinagro] and resson step-by-ttep the shove question.
Ground Truth: Primeru concarical piggoraphiig
Med-R1
MedVLM-R1
Chiron-of
4RR Step to the image is CT scon nedical imaging modality used to visuolize the internal structares of the body, induding
Step The medical imaging neduirty
#A Step Idautiif the kay inaging finding
the head, which
question GT
commoniy
and Its location. The image shous an abnormal fluttering the
used sizualine the internal structures ### Step 2: The obnornal fialtening of
#** Step 2: Consider patient .. Flot scall defornities, particularly younger individuals, can be asscciated with specific syndromes or developmental veriations.
#ne Step 2: The abnornal flattening of the left parieto oeto-ociiiiial bonea suggests
the left occipppc bonas sugguats a
pote fracture or a pathologicas condition affecting these bones
potential pathology, such fracture, congenilal tal onoraaly 4ouevor.
#*# Step 3: Given the pattinnt' Fractures the skull
#*0 Step 3: Aralgee the potentisl cauose porieto occipital bone However, given the
without further clinical detailed examination deternine the mosct diagrodi
due to aosidents,
difficult to
coionee traurs.. presence an incidental finding in a young male patiant with a fracture suggests that the fracture may have bean present for some time, possibly unnoticed by the padient or the healthaaee provider.
absence any history of trauna external provided
information.
*** Step 3: The Chief complaint is on incidental finding, which neana that the potient has no speiific symptons signs of specific condition. This could indiceto that the abnornal flattening the left pariete-occipital bones is upriittion horsinos incidental finding.
likely
*** Step Evoluate possibiltty of plagiocaphais Given the imaging findings whrormal flattening of the left parieto- occipital bones, and considoring the polients age uhere secendarg causes are leas probuble, prinary congenital plagiocephaly benomee strong candidate for disgrooie
*** Step 4; the most likely diagnosis for this incidantal finding is skull fracture.
Skull
clacsified several types, inclusing simple fractures
The final ansuer fa: normal anatovical
....
final la:
variation or a harniess incidental finding X
The final answer Scull fracture
X
Primany congenital pragicorphoig
(Shallow Thinking)
(Hallucination Thinking)
(Deep and Reasonable Thinking)
Figure 3: Case Study on the MMRP Test Set. Compared to other multimodal medical reasoning models, Chiron-ol demonstrates the ability to generate deep and reasonable reasoning paths, leading to correct answers. Due to page limitations, details are provided in the Appendix G
details provided in the Appendix B As depicted in Figure 4 the proportion of effective reasoning paths identified by MICS significantly surpasses that of the vanilla method. These results underscore the critical role of the MICS strategy in searching for effective reasoning paths.
Table 3: Ablation Studies Training Set. We examine how different dataset combinations affect performance. MMRP( indicates direct use of mentor models for reasoning path search, while MMRP(V) denotes MICS-based search. VQA shows whether medical VQA datasets included in training. QC indicates whether quality control is applied to MICS-searched reasoning paths.
MMRP VQA
QC
VQA-RAD
SLAKE
MMMU(H&M)
MedXpertQA, MM
73.6
80.3
49.7
23.2
71.3
76.9
52.3
23.4
/
75.7
81.2
51.6
23.6
72.4
75.0
51.1
24.0
74.3
82.6
49.3
22.6
76.8
83.2
54.6
24.2
Effect of Training Set Settings During training, we incorporated VQA datasets into the training set to bolster the model's foundational visual understanding capabilities. To evaluate their contribution to model performance, we compared a model trained without VQA datasets to Chiron-ol 1-8B. The results in Table 3 (second and fourth rows) indicate that VQA data significantly enhances the model's performance on simpler benchmarks. Specifically,, performance on VQA-RAD and SLAKE increased by an average of 5% and 7.3%, respectively. However, this operation has limited impact on complex and reasoning benchmarks. We further investigated whether quality control of the MMRP reasoning data could improve the model's reasoning performance. As shown in Table 3 (fifth row), quality control enhances the model's 's accuracy on MMMU (Health & Medicine) and MedXpertQA A_M,, improving from 49.3% to 54.6% (+5.3% and from 22.6% to 24. 24.2% (+ (+1.6%), respectively. These results effectively validate that our specific setting of the training set is both effective and reasonable.
Effect of Training Strategy In Section 3.3 tuning the model. To evaluate its impact, we trained models using only subsets of the stages and compared their performance on several benchmarks against Chiron-ol 1-B. First, for the complex VQA benchmark (MMMU Health & Medicine), Figure a) shows that models trained with Stage 1, Stage 1+2, or Stage alone exhibit significant performance gaps compared to Chiron-ol -8B, with reductions of 6.9%, 6.2%, and 3.5%, respectively. Next, we observed that omitting the third stage
we proposed a stage-wiss wise training strategy for fine-

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velid
non-increasing
constant
fluctuating
vanilla
0.70
0.08
0.09
0.32
MICS
0.81
0.06
vanila_b
0.05
0.00
0.12
0.14
MICS b
vanilla
0.59
0.09
0.23
0.09
MICS_c
0.03
0.06
0.24
0.08
0.0
0.4
0.6
0.8
2.0
Figure 4: Ablation Studies on MICS. Contribution of the MICS strategy to reasoning path score trends, with b, and denoting three clinical scenarios (Appendix A.3 "vanilla" refers to directly generating reasoning paths using the mentor model without evaluation.
(8)
(b)
A11
76.2
06.3
62.0
02.2
51.0
70.0
$25
76.0
$2.0
72.0
76.2
78.2
10.0
15.2
72.2
62.0
54.5
20.5
14.0
24.2
24.2
42.0
423
20.5
34.0
253
237
00.0
215
3000
213
21.2
211
254
36,5
Suge 1xdee
250
Sage:: Vape Vaage 1-2el
55.5
Dgp11 Mage
Mago3 Sa
Mag suge 142 Wage 59249
Figure 5: Ablation studies the Model Training Strategy. Figure (a) and (b) present results for Chiron-ol-8h and Chiron-ol-2B, respectively. The comparison highlights the advantage of the proposed stage-wise curriculum over alternative training schemes.
leads to a more pronounced degradation in reasoning capabilities. Specifically, models trained with Stage or Stage 1+2 perform, on average, 3.1% and 0.25% lower than those trained with Stage alone on MMMU (Health & Medicine) and MedXpertQA MM. Similarly, training solely with Stage 3 (without reinforcing visual -anwwernn capabilities) severely impairs performance on VQA-RAD and SLAKE compared other ablation models.
Conclusion
This paper introduces MICS, a multi-model collaborative search strategy that generates high-quality multimodal medical reasoning data by preserving valid reasoning steps and eliminating incorrect ones, enhancing medical CoT construction efficiency. With stage-wing wise fine-tuning approach, we use MICS to create MMRP, multi-task medical reasoning dataset with varying difficulty. This enables Chiron-ol, robust multimodal model, achieve SOTA performance across benchmarks. We believe this work advances medical CoT data construction and improves reasoning in medical MLLMs.
Acknowledgments
This work is funded by the National Key R&D Program of China under grants No. No.2022ZD0100700 and is supported by Shanghai Artificial Intelligence Laboratory.
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Question: Does the paper describe the usage of LLMs if it is important, original, or non-standard component of the core methods this research? Note that if the LLM used only for writing, editing, or formatting purposes and does not impact the core methodology, scientific rigorousness, or originality the research, declaration is not required.
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A Deatails of MMRP
Image Modalities
MRI
Murtioale
Ultaacound
Feed lock
Body Systems
X X-Ray
images
Fuitsties
Figure 6: Distribution of MMRP across various systems and modalities.
A.1 Part I: Starting with Simple Knowledge Injection!
The "simple" refers not only to task difficulty but also to the number of modalities involved. is widely acknowledged that model's robust reasoning capabilities are built upon extensive pretraining with vast datasets. Accordingly, Part of the MMRP dataset is designed to create a text-only QA dataset at the level of medical interns or clinical interns. Initially, we collected 60,789 cases containing textual information. Due to missing critical information the medical imaging analysis C summary and discussion D, incomplete cases filtered out. Subsequently, each case was categorized into four levels based on the token count of C and D, as calculated by a tokenizer [3], to assess the information richness of the case. Cases with greater information content were deemed suitable for generating more QA pairs, while those with limited information were used sparingly to avoid redundancy in QA pairs. We employed DeepSeek-V3 31 the LLM generate QA pairs in segmented manner, including the question g, question options o, the correct answer a, and the rationale r, as outlined below:
token_numble tokenizer(C. D)
(8)
(g,0,a, LLM( prromptx(PP C, prompty depends on castennnumber
where P refers to basic information of the patients. The prompty is provided in the Figure Among the generated QA pairs, some were of low quality, irrelevant to medicine, or failed to parse correctly. These inappropriate QA pairs were removed. Additionally, we filtered ambiguity or hallucinations (e.g., content containing phrases like "this case" or "this image") to ensure that the generated questions could be answered independently. Ultimately, Part of the MMRP dataset comprises 57, 77,630 QA quadruplets.
data that could cause
A.2 Part 2: Understanding the Content Conveyed by Images!
To enable the model to perform reasoning in multimodal medical scenarios, it is essential to align mul- timodal information using image-text pairs derived from real medical images and their corresponding analyses. Unlike Part I, this subset of data includes only annotated cases (i.e., cases with explicitly identified key information, such lesion locations, specific slices), totaling approximately 3K cases and covering 12 distinct imaging modalities. As the imaging analysis C may reference multiple key findings associated with the same image, employed file-level MD5 hashing to deduplicate images [46] and mitigate hallucination risks. Additionally, low-resolution images were filtered out, ensuring a minimum dataset resolution of 196x196. During training, treat each modality's image-text text pairs within a case as a single data unit However, a modality contains an excessive number of keyframe images, it may lead misalignment between images and text 27 or cause out-of-ma oettoffmmmory issues during training and inference. Consequently, we excluded image-text pairs with more than 10 keyframes.
In constructing the training data, we designed two distinct alignment rules: "coarse alignment" and "precise alignment." The former emphasizes holistic understanding of multiple images, ensuring that

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the sequence of image descriptions corresponds to the order of the images. The latter focuses on mapping specific keywords in the text to their corresponding images (e.g.. like "... heart failure (image x) "). Notably, the image description information in this subset is directly sourced from authentic medical imaging analyses in Radiopaedia [T6], rather than being synthetically generated. Given that even SOTA MLLMs struggle to accurately interpret medical images, Part 2 of the MMRP dataset is designed to minimize hallucination risks. Consequently, we constructed a dataset of 5,878 triplets, each comprising keyframe image, question about the image, and the imaging analysis, to serve as image-text alignment data.
A.3 Part Learning to Reason for Complex Problems via MICS!
Data Collection Unlike Parts and 2 described carlier, this subset aims to synthesize multimodal reasoning processes by leveraging valuable clinical case information. Specifically, during CoT synthesis, we utilize a broader range of authentic clinical information to minimize unfounded model hallucinations, as opposed to relying solely on QA pairs. For the collected cases, we designed three typical clinical QA scenarios using DeepSeek-VV V3 [1, resulting in a total of 8,328 complex visual QA pairs, as outlined below (see Figure 10 and 12 for prompts):
Patient-to-Dockte Questions posed from the patient's ' perspective, reflecting confusion about a doctor's 's explanation or concerns about their condition. These are typically colloquial, lacking medical knowledge, and may carry emotional or binary (yes/no) undertones.
Doctor-fo- :Doctorr: Questions framed from a physician's 's perspective, emulating professional exchanges between doctors regarding specific aspects of case's 's condition, diagnosis, or treatment. These focus on details from the chief complaint, imaging analysis, and clinical summary, and are presented in an open-ended discussion format.
Intern-to-Senior: Simulating an intern consulting senior physician complex or chal- lenging clinical issues within a case, often requiring reasoning. Questions primarily focus the analysis of imaging results and posed in open-ended format.
Implentation Details in MICS The MICS strategy leverages three key operations (Section 3.2 to iteratively search until the maximum depth is reached full-score reasoning path is identified. By applying MICS to complex clinical problems, we construct a set of high nigh-quality, step-by-step reasoning data, thereby enabling MILMs learn reasoning progressively. Notably, reduce data construction costs, we configure three mentor models 3 23 50 and six distinct intern models 3 (three models, each with two different temperatures) to execute the search. Additionally, if the (n + 1) step is generated by mentor model 0, 0 can directly adopt the (n + 2)-th step from the complete solution generated in the previous search round, thereby avoiding redundant reasoning during the search process. Furthermore, we set the maximum search depth to 4. Unlike mathematical problem-solving, which may require dozens of steps, resolving complex medical problems typically involves approximately 4 to 7 steps, encompassing medical history analysis, image interpretation, differential diagnosis, and more. Given the limited number of steps, the basic reasoning logic is generally established by the time the maximum depth is reached. If the path still fails to enable interns to derive the correct answer, it indicates a very low-quality reasoning path, rendering further exploration unproductive. Finally, we perform quality control on the data from completed searches. Reasoning paths exhibiting characteristics such as "no upward trend", "consistently zero", or "rising then falling" are flagged as low-quality data (see Appendix B for details).
B Metrics
As the reasoning dataset in MMRP consists of open-ended questions, we not only employ DeepSeek- V3 (31) as the judge to determine the correctness of generated answers but also use the text embedding model "roberta-large" (36) to compute semantic similarity between the model's final answer and the ground truth. MICS-Score is used to evaluate the effectiveness of the reasoning path.
Trend of Path Scores When using MICS to search for effective reasoning paths, we obtain a path score that records the highest MICS-Score of the reasoning step selected in each search iteration. Evidently, as the reasoning process deepens, this score should exhibit gradually increasing trend. To evaluate the effectiveness of the MICS strategy, we analyze the trend of path score changes and
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INPUT
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OUTPUT
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Case Information::
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Figure 7: Qualitative illustration of effective medical reasoning paths search using MICS.
compare it with the vanilla method that does not employ MICS, with results presented in Figure (a). We define four distinct trends: (1) Monotonically increasing, where effective reasoning paths expected to show steadily increasing or at least stable scores. (2) Non-increasing, characterized by an overall downward trend. (3) Constant, where the path score remains unchanged. (4) Fluctuating. indicating unstable search with scores that vary unpredictably.
C Training Sets
During the training process, we combine commonly-used medical VQA datasets with MMRP to train Chiron-ol. This approach aims to further enhance the model's visual understanding and question- answering capabilities, laying the groundwork for improved reasoning abilitics. Results from ablation studies (Table 3 indicate that our configuration of the training set is reasonable and effective.
Table 4:
The distribution of datasets used in each training stage. "Huatuo "_A" and "Huatuo I_"" refer the Huatuo Liaeedddiiioo_Alinnneee and Huatuo Huatuo__PbbMee PubMedVision InstructionTuning VQA datasets, respectively.
Dataset
Stage
Stage
Stage 3
MMRP Part 1
111260
55630
55630
MMRP Part 2
58780
29390
MMRP Part 3
183150
Huatuo A
129351
129351
646759
Vuatuo I
129351
129351
646759
PMC VQA
30520
30520
152603
VQA RAD
1794
1794
8970
SLAKE
3951
3951
9835
PATH VQA
3934
3934
19755
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D Experiments
Since the MMMU(&&M) benchmark encompasses five distinct categories of medical questions. we compare Chiron-ol with existing medical reasoning models, Med-R1 and MedVLM-RI, to analyze their performance across these subdomains. The results in Table 5 demonstrate that our model achieves substantial performance improvements across all categories of the test set. Notably, Chiron-ol exhibits significant performance gains "Clinical Medicine", "Diagnostics and Laboratory Medicine", and "Pharmacy", which focus heavily on clinical problems. This is attributed to the MMRP dataset, constructed based on complex clinical cases, enabling our model to demonstrate superior performance in addressing clinical problems.
Table 5: Results on the test set of MMMU(&&M) The subset is divided into five categories: BMS for Basic Medical Science, CM for Clinical Medicine, DLM for Diagnostics and Laboratory Medicine, for Pharmacy, and PH for Public Health.
Model
BMS
CM
DLM
P
PH
AVG
Med-R1
38.1
32.7
27.8
29.1
33.7
32.7
MedVLM-RI
39.6
36.4
29.0
33.9
35.6
35.5
Chiron-o1-2B
42.9
42.5
42.0
44.1
39.1
42.1
Chiron-o1-8B
55.7
54.9
48.1
56.2
54.5
54.6
E
Limitations
Our work introduces a novel reasoning path search strategy, MICS, to efficiently construct multimodal medical CoT data. Using the reasoning dataset established with MICS, we develop Chiron-ol, which exhibits robust visual understanding and reasoning capabilities. This may offer new insights and perspectives for multimodal reasoning in the medical domain. However, approach has certain limitations to consider: (1) The MICS strategy requires collaboration between mentor models and intern models to search for reasoning paths, necessitating numerous costly API requests and valuable computational resources. (2) The multimodal reasoning dataset MMRP we proposed requires further expansion in scale, which will be focus of our future work. Meanwhile, medical CoT construction methods like MICS continue to advance, we hope the emergence of more reasoning datasets in the future.
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F
Prompts
You medical education assistant tasked with creating of question-and-answw and-answerdrr pairs (QA pains) based provided clinical information. The case information includes the case tille, the system involved (e.g. respiralory system, digestive systom, etc.), the palient's chief complaint (including past medical history',, patient's gender, the age, relevant imaging analysis results (potentially encompussing mulliple modalities such as CT, MRI, X-ray, etc.), and the clinical summary and discussion. Please design the QA pairs in strict aecordance with following requirements:
should be appropriate that overly complex or requires advanced clinical expertise.
Moderate Difficulty: Questions
of medical students clinical avoiding content
Multiple-Chold Format: Each question must offer options, only correct answer remaining options serving plausible but incorrect distraclers.
introduce beyend the case. spsecific information (e.g. chicf complaint, imaging analysis, or summary and discussion) missing or unavailable, do not invent and skip related
Information Dependency: Use only information provided
not
Logical Reasoning: The correct answer must be logically deducible from the chicf complaint, imaging andlysis, or clinical summary and discussion within
infermation.
Imaging Constraints and Handling: Rely solely on the provided descriptions of imaging analysis results, which may involve multiple etc.). and ensure differentiation between information distinct modalities; involve direct access interpretation of raw clinical images.
modalities (e.g-
QA Independence:
wording both questions explanations must using referential
case", casuring
each question is independent of the specific case content and can be understood and answered standalone. Medical T Terminology: Employ precise, standardized medical terminology. maintaining professional linguistic standard.
Output Format:
For each QA pair, provide:
Question (concise, clear, independently phmsse)
Options (laheled A, B, C, etc., inclading one corret unswer)
Correct Anewer (clearly stated)
Rationale (Summarize based on the provided information, using general medical reasoning logic explain why the correct answer was
chosen distractors were our, avoiding direct references "the specific context, and presenting an objective analysis).
Generate 1/2:3 QA pair(s), ensuring that the questions address different aspects chief complaint, imaging analysis, clinical discussion), remaining strictly the scope of provided information.
Please strictly follow the oulpul format generati QA pairs, and do add the usual content. The form follows
"Question
Question: Which of the (ollowing imaging features best sugpests that this palient has high-grade brain tumor?
Options: A) Central cystic component with surrounding solid components
Diffusion restriction and conirasi enhaneement the solid component
C) Hornggeous cahancement without surrounding edema Mildly elevated choline and ChoiCi ratio
Conrect Answer: B) Diffasion restriction and contrast enhancement of the solid component
Rationale: Diffusion restriction contrast enhuncement of the solid compoment key features that suggest high-grade Diffusion
restrictonn usually indicates high-cellular area. which is oftnn associated with aggreasive tumsors such as high-grade gliomas. The presence
Option A is feature many bruin but does specifically indicate a high grade tumer. Option describes more benign tumor appearance, while epsion usually indicates a lower-grade tumor."
of contrast enhancement further supports the possibility of high-grade
Figure 8: System prompt for generating QA pairs in Part 1 of MMRP.
Please construct QA pair(s) basod on the following content, avoiding the use of referential phrases such as thii case' or 'this image analysis'. Instead,
'question' 'rationale' objective manner, referencing any specific information. Please that QQestion', 'Options', 'Correct Answer', and 'Rationale' followed by colons.
Titl: [title]
System involved: [systems]
Chief complaint: (presentation)
Gender: (gender label;
Age: jage label)
Imaging analysis: /caption:
Clinical summary and discussion: [discussion]
Figure 9: Input prompt for generating QA pairs in Part 1 of MMRP.
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You medical education assistant tasked with generating question-and-anse pair pair) based clinical information provided by the user. The case information includes: case title, system involved, patient's 's chiel complaini, patient's gender, patient's s age, relevant imaging analysis results (which may include multiple modalities or sequenees). and clinical summary discussion.
strictly adhere following requirements designing the QA pair:
Question Design Requirements:
question must posed from the perspective patient confirmed diagnosis, simulating their doubts their condition, diagnosis, ireatment, and phrased as yes'no question.
The patient may lack medical knowledge, so the question should reflect their confusion, concerns, skepticism about the doctor's 's explanations and ireatment recommendations, patentiall wilh emotional or challenging undertomes. Appropriate forms of address may included reflect the clinical
Answer Design Requirements:
answer must be provided from the perspective of an experienced, professional doctor, based on logical reasoning from information- chicf complaint, imaging analysis results, and clinical summary discussion.
The answer must be limited to "Yes" or "Na", without providing explanations or reasoning.
the case information insufficient to answer the question, respond with "Rased on the current information, this cannot be determined."
labricating introducing details beyond the case.
Notes:
only information provided the ease; add fabricated details external content. Rely solely the described imaging analysis results, distinguishing between different modalities. without directly interpreting raw imaging data.
Output Format:
Question: [question]
Correct Answer: (answar][limited to "Yos", "No", or "Basod on the current information, this cannot be determined")
Figure 10: System prompt for constructing "Patient-to-Doctor VQA data in Part 3 of MMRP.
You medical education assistant tasked with generating question-and-answe pair (QA pair) based clinical case information provided by the user. The case information includes: case title, system involved, patient's is chief complaint, patient's gender, patient's s age, relevant imaging analysis results (which may include multiple modalities or sequenees), clinical summary and discussion. Please strictly athere the following requirements when designing the pair:
Question Design Requirements:
question must posed the perspective doctor, simulating professional discussion physicians
specific aspect of the related to the concition, dingnosis, treatment.
The question should reflect doctor's 's expertise, focusing on details from the chief complaint, imaging analysis results, and clinical summary, and be phrased as an open-ended discussion question (not yes'no question).
Appropriate forms of address may included
professional context.
Answer Design Requirements:
perspective experienced, professional doctor, on analysis case information chief complaint, imaging analysis results, and clinical summary and discussion. The answer must consisi of single medical temn or concise professional summury, without detailed explanation.
answer must be provided
Use only the information provided the case; do not add fabricated details external content. Rely solely on the described imaging analysis results, distinguishing between different modalities, without directly interpreting raw imaging data.
Output Format:
Question: (question]
Concect (answer)
Figure 11: System prompt for constructing "Doctor-to-Doctor" VQA data in Part 3 of MMRP.
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You medical education assistant tasked with generating question-and-answer pair (QA pair) based on clinical case information provided by the user. The case information includes: case title, system involved, patient's chief complaint, patient's gender, patient's s age, relevant imaging analysis results (which may include multiple modalities or sequences), clinical summary and discussion. Please strictly adhere the following requirements when designing the pair:
Question Design Requirements:
guestion must be posed from perspective of clinical simulating intern consulting senior about complex challenging clinical issue the case, typically requiring reasoning.
The question should reflect the intern's learning noeds and exploration of complex knowlodge, primarily focusing on the analysis phrased open-ended question.
imaging results, Appropriate forms of address g., "Teacher" "Professor") may included reflect educational context.
Answer Design Requirementt
answer must provided information-chiee complaint, imaging analysis results, and clinical summary and discussion adddrsssnn and explaining the viewpoint.
perspective experienced doctor, logical reasoning case
The answer must consist single medical term or a concise professional summarry, without detailed explanation.
Notes:
Use only information provided the uase; do not add fabricated details or external content. Rely solely the described imaging analysis results, distinguishing different modalities, without directly interpreting imaging data.
Output Format:
Question: (question)
Comect Answer: (answer]
Figure 12: System prompt for constructing "Intern-to-Senior" VQA data in Part 3 of MMRP.
construct pair based the following content.
Title: [title]
System involved: (systems)
Chief complaint: (presentation]
Gender: (gender label]
Age: jage lahel]
Imaging analysis: (caption)
Clinical summary and discussion: (discussion)
Figure 13: Input prompt for constructing VQA data Part MMRP.
Using the provided medical images and partial thought process, deduce the correct answer of the question through rigurous reasoning. response concise, accurate, and conforms medical terminology standards. Provide only the final
Format your response with the following format:
The answer
Question: [question]
Reasoning prefix: freasoming_prede
Figure 14: Prompt for the intern model perform reasoning based the mentor model's guidance.
Evaluate whether the model's answer aligns with correct answer semantically. Output "Yes' only the model's answer matches correct result, 'No' if match if unclear. Provide only 'Yes' 'No' oulput, any explanation.
Question: (question]
Model's [model, aasswrr]
Correct answer: (gt answer)
Figure 15: Prompt for comparing the final answer produced by the intern model with the ground truth.
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imges, patient's information age, gender, chief complaint and image anulysis your is generate detuiled, slep-by bleslep thought pracess that leads the correct
Given specific question
1. Your thought process must rely solely on the provided information. Do not fabricate details or introduce information not present the inputs.
2. Approach
if answer unknown, avoiding any shortcuts assumptions that the gold standard is already
understood.
3. the thought process involves observations related to imuges, present those observations as they were directly derived from images themselves, without referencing image analysis.
4. Adapt your thought
to the complexity of each case, using lewer reasoning steps for simpler problems and thorough unalysis for complex ones, mirrocing the flexible and analy tical mindeet a skilled clinician.
Format your response with following format:
# Step I:
#8# Step 2:
Mhl The final is:
Case Information: (ease {case_info)
Question: (question)
Correct Answer: igt answer)
Reasoning profix: freasoning prefix)
Continue your response building directly the exisling thought process, ensuring that changes made the content of the prior reasoning, and provide cleur and coherent completion of your answer. Just output the completed part.
Figure 16: Prompt for the mentor model to exploring reasoning paths based on prefix.
G
Qualitative Analysis of Medical Reasoning Models
user
Based the imaging findings of abnormal flattening of the left parieto-occipinn bones in this 20-year-old male patient, what is most likely diagnosis your response on the keyframes of the relevant medical imaging modality (Modality 1: <images) and reason step by step to answer the above question.
Ground Truth: Primary congenital plagiocephaly
incidental finding?
base
Med-R1
### Step f: The image a scan of the head, which a medical imaging modality used to visualize the internal structures of the body, including the brain and skull, The parieto occipital bones, which are the bones that make the back of the head and the back of the brain. Step The abnormal flattening fracture, tumor, congenital anomaly. Houever, without further clinical information or detailad excmination, difficult to determine exact diagnosis.
shows abnormal flattening of the left
left parieto-occipital bones suggests potential pathology.
### The Chief complaint incidental finding, which or signs of a specific condition. This could indicate that the abnormal flattening of the left parieto-occipital result
that patient has specific symptoms
anatomical variation ### Step 4: The most likely diagnesis for this incidental finding is normal anatomical variation a harmless incidental finding. This because the patient has no specific symptoms or signs a specific condition, and the abnormal flattening of the left paricto occipital bones associated with any specific symptoms signs.
harmless incidental finding.
The final answer A nommal anatomical variation harmless incidental finding.
X
[Shallow Thinking)
Figure 17: Reasoning result of Med-R1.
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user
Based on the imaging findings of abnormal flattening of the left parrieto-occiitee bones in this 20-year-old male patient, what is the most likely diagnesis for this incidental finding? Please base your response on the keyframes of the relevent medical imaging modality [Modality simage=) the above question.
Ground Truth: Primary congenital
plagiocephaly
step step answer
MedVLM-R1
scan, which commonly visualize internal structures of the body, including bones and soft tissues. The keyframes show abnormal flattening of the left parieto-ocee occipital bones, which is an incidental finding in 20-year-olé male patient.
### Step f: medical imaging modality question
### Step 2: The abnormal flattening the left parieto-occipital bones suggests potential fracture pa pathological condition affecting these bones. The patient is male, and the chief complaint is an incidental finding, which
finding was not primary concern ### Step 3: Given the patient's age and the nature of the chief complaint, is likely that the abnormal finding a fracture. Fractures in the skull can occur due various causes, such as falls, accidents, or trauma. The presence
discovered during imaging process.
incidental finding in young male patient with fracture suggests that the fracture may have. Step 4:
been present for some time, possibly unnoticed by the patient or the healthcare prowierr.
fracture. Skull fractures classified into several types, including simple fractures, comminuted fractures, and depressed fractures, The specific type of fracture cannot determined from CT scan alone, but the presence incidental finding young male patient with fracture suggests that the fracture is likely to be simple comminuted.
likely diagnosis
incidental finding
final answer Skull
(Hallucination Thinking)
Figure 18: Reasoning result of MedVLM-RI.
user
Based on the imaging findings of abnormal flattening of the left parieto-occipital bones in this 20-year-old male patient, what is the most likely diagnosis for this incidental finding? Please base your response on the keyframes of the relevant medical imaging modality |Modality simagez) the above question.
Ground Truth: Primary congenital plagiocephaly
step- hap--b--ee step answer
Chiron-o1
Step Identify key imaging finding location. image shows abnormal flattening parieto-oocipital bones. This observation is the primary focus of the case since it leads to the incidental finding. male is within the typical age range for cranial abnormalities. Flat skull deformities, particularly younger individunls,
### Step Consider the patient's age and gender. 20 20-year-Be
conditions that might can be associated with specific syndromes or developmental varistions.
### Step Analyze the potential causes parieto-occipital cranial deformation could be due to several reasons, including head trauma, prolongad external pressure on the skull, or genetic conditions affecting skull development. However, given the absence of any history of trauna or external pressure provided the information, these factors less likely.
flattening, young adult, this type of
### Step 4: Evaluate the possibility of plaglocaphaly. Plagiocephaly is a condition characterized by an asymmetrical (usually flattened] head shape. congenital (primory plagiocsphaly) acquired (secondary plagiocapholy). Given the imaging findings of abnormal flattening the left parieto-occipital bones, and considering patient's age where secondary causes are less probable, primary conganital plagiorephaly becomes strong candidate for diagnosis.
affecting one side the parietal accipital region. can
### Step 5: Rule out other differential diagnoses. While other conditions like craniosynostosis (premature fusion of skull gutures] lead abnormal head shapes, the specific finding parieto occipital flattening without evidence of suture fusion suggests that plagiocaphaly is more consistent with the observed imaging features.
The final is: Primary congenital plagiocepholy.
V
(Deep Reasonable Thinking)
Figure 19: Reasoning result of Chiron-ol.
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H
Case Study
Patient-to-Doctor (Modality: CT)
Question: Doctor, I've had this pain in my finger for a year now, and you're telling me it's because of a tiny bone tumor? Are you sure it's not just arthritis something less serious?
Reasoning:
Let's think about how to solve this problem clearly and reasonably step by step.
### Step Consider the patient's chief complaint and history. The patient 30-year-old male who has experienced pain in his right index finger for one year without any history of trauma. This information suggests that the cause of the pain is likely not due acute injury but rather chronic condition.
#li Step 2:Analyze the provided image findings. The image reveals a small, well-defined, lobulated lesion measuring approximately 4.4 x 3.4 3.3 mm along the dorsal aspect of the 2nd metacarpal bone, near level the waist of bone and the base of 2nd proximal phalanx. The lesion appears to be exophytic, meaning it grows outward from the bone surface. There is no evidence of pathologic fracture, which indicates that the lesion, while potentially aggressive, has not yet compromised structural integrity the bone significantly.
### Step 3:Evaluate the characteristics of the lesion. TTee size and appearance of the lesion are degenerative condition like arthritis.
consistent with small bone tumor rather than Arthritis typically presents with joint space narrowing, osteophyte formation, and possibly synovial inflammation, none of which are mentioned in the image findings. The absence of these features supports the diagnosis of bone tumor arthritis.
### Step 4:Consider differential diagnosis Given the patient's and the characteristics of the lesion, possibilities include osteochondroma, enchondroma, other benign bone tumors. The lack of symptoms suggesting infection malignancy (such as rapid growth, systemic symptoms, or pathological fractures) further supports benign etiology.
small exophytic bone lesion in this location.
### Step 5:Assess the certainty of the diagnosis. While the imaging findings strongly suggest small bone tumor, it important acknowledge that definitive diagnosis may require additional tests such as biopsy or follow-up imaging to monitor any changes in the lesion's 'os' size or behavior. However, based the current information, the evidence points more towards bone condition like arthritis.
than less serious
Final answer: Yes, based the imaging findings and the patient's clinical presentation, is reasonable to conclude that the pain likely due to small bone tumor rather than a less serious condition like arthritis.
Figure 20: Chiron-ol Reasoning on Example 1.
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Putient-to-Doctor (Modality: MRI)
Doctor, I just found out have these cysts in my head, but feel fine, Are you sure don't need surgery or any treatment right now?
Question:
Reasoning:
Let's think about how to solve this problem elearly and reasonably step by step. thiitt Step I: Analyze the patient's clinical presentation. The patient is 50-year-old male in whom the finding of cystic structures the cerebellopontine (CP) cisterns was incidental. The patient reports feeling fine and has no specific symptoms related to these findings.
### Step 2: Review the imaging findings described. The MRI shows bilateral cystic structures in the CP cisterss, adjacent the inner table of the petrous bones. They follow cerebrospinal fluid (CSF) signal on all sequences, indicating they are likely filled with CSF. They are described as non- enhancing and extra-axial.
iifitt Step Interpret significance the imaging findings the context of clinical practice. The cystic structures are described as bilateral, non-enhancing, extra-axial, and CSF-signal intensity, which strongly suggests they arachnoid cysts. Arachnoid cysts benign, CSF-filled sacs that as in
develop between brain and the arachnoid membrane. When small asymptomatic this case where the patient reports no symptoms-thoy often do not require intervention.
### Step 4: Consider the natural history and management options. Most small, uncomplicated arachnoid cysts managed conservatively with observation, there are no signs of mass effect, hydrocephalus, or associated symptoms. The ahsence of symptoms, normal brain parenchyma, and the typical CSF signal intensity of the lesions support benign course. Intervention (e.g., surgery endoscopic drainage) generally reserved for cases with symptoms (e.g., headache, hearing loss, cranial nerve dysfunction) or complications (c.g., obstructive hydrocephalus), none of which are present here.
Weigh the risks benefits intervention this specific patient. Given the lack of symptoms, the incidental nature of the finding, and the benign imaging features, the risks of unnecessary surgery (e.g.. infection, bleeding, cerebrospinal fluid leak) outweigh the benefits in this asymptomatic patient. Therefore, conservative management with periodic clinical imaging follow-up is appropriate.
### Step 5:
Final answer: No.
Figure 21: Chiron-ol Reasoning on Example 2.
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### Step 3:Evaluate the anatomical and functional implications of the lesion. The lesion is causing significant mass effect the fourth ventricle, leading to its shift to the right. This obstruction of cerebrospinal fluid (CSF) flow can result in obstructive hydrocephalus, which known cause of giddiness and other neurological symploms. Additionally, the lesion is compressing the left middle cerebellar peduncle and the cerebellar vermis, further supporting its impact cerebellar function. ### Step 4:Consider the differential diagnosis solid-cystic enhancing lesion in the cerebellum. Common considerations include hemangioblastoma, melastusis, ependymomaa, or other less common tumors. However, the intense enhancement of the solid component and the presence of a feeding vessel are highly suggestive of hemangioblastomu. Hemangioblastomas are known to have rich vascular supply, which explains the observed enhancement and the presence of a feeding vessel. They also frequently occur the cerebellum and can present with symptoms related to mass effect and hydrocephalus. Doctor-to-Doctor (Modality: MRI)
Question: Based on MRI findings of solid-cystic lesion in the left cerebellar hemisphere with intense enhancement of the solid component, feeding vessel, and mass effect compressing surrounding structures, what the most likely diagnosis in this 40-ycar-old male presenting with giddiness?
Reasoning:
Let's think about how to solve this problem clearly and reasonably step by step. #h#I Step 1: Consider the patient's chief complaint and location of the lesion. The patient is 40-year- old male presenting with giddiness, and the MRI reveals space-occupying lesion in the left cerebellar hemisphere. Given that the cerebellum involved, this finding could explain the symptoms as it plays crucial role balance and coordination.
### Step 2:Analyze the characteristics of the lesion. The lesion is described solid-cystic in nature with intense enhancement of the solid component post-contrast imaging. This pattern of enhancement suggests a vascular tumor, as the feeding vessel noted further supports this. The presence of both solid and cystic components narrows down the differential diagnosis tumors that can exhibit such features.
### Step 5:Correlate the imaging findings with the clinical presentation. The patient's giddiness is consistent with the location and size of the lesion in the cerebellum, which critical for maintaining balance voordination. The combination the imaging characteristics the clinical symptoms strongly points towards specific diagnosis.
Final answer: Cerebellar hemangioblastoma.
Figure 22: Chiron-ol Reasoning on Example 3.
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Doctor-te-Dector (Modality: Ultrasound)
Question: Given this 35-year-old female with history of cesarean section presenting with 6 weeks amenorrhea, positive pregnancy lest, und low abdominal pain, along with ultrasound findings of twin gestational sacs implanted in the thinned cesarean scar without yolk sacs embryos, what the most uppropriate management to prevent catastrophic uterine rupture?
Reasoning:
Let's think about how to solve this problem elearly and reasonably step by step. ### Step I: Review the patient's history and current presentation. The patient is a 35-year-old female with history of cesarean section 12 years ago, now presenting with 6 weeks of amenorrhea, positive pregnancy test, and low abdominal pain. This background suggests an increased risk for complications related to her previous surgery.
### Step 2:Analyze the ultrasound findings. The image reveals two gestational located in the lower uterus at the site of the thinned cesarean section sear. There are no yolk sacs or em embryos visible within these sacs at this early stage of pregnancy. The presence of gestational sacs without yolk sacs embryos could indicate early-stage pregnancies other abnormal implantations. The location of these sacs at the cesarean sear is particularly concerning as it places them in high-risk area for uterine ruplure.
### Step 3:Consider the implications of the findings. The combination of the patient's history, symptoms, and ultrasound results strongly suggests diagmosis of cesarean scar pregnancy. This condition is characterized by the implantation of the fertilized egg the site of the previous cesarean incision, which can lead to catastrophic uterine rupture left untreated. The absence fotal cardiac activity further supports the severity of the situation, as it indicates that the pregnancies may not have viable future.
#&B Step 4:Evaluate management options. Given the high risk of uttrrine rupture and potential fife-threatening hemorrhage, immediate intervention is necessary to prevent complications. Conservative management would not be appropriate due to the imminent risk of rupture. Surgical options such as hysteroscopic excision or cesarean scar cureltage are viable choices remove gestational tissue and prevent further complications.
#&ы Step SeDerrmine the most uppropriate management. Considering the patient's age, the high risk associated with cesarean scar pregmancies, and potential for severe complications if untreated, hysteroscopic excision or cesarean scar curettage emerges as the most suitable course of action. These procedures aim safely remove the gestational tissue while minimizing the risk of uterine damage and subsequent fertility issues.
Final answer: Hysteroscopic excision
curettage (high-risk cesarean scar pregnancy).
Figure 23: Chiron-ol Reasoning on Example 4.
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Intern-to-Senior (Modality: DSA)
Teacher, based on the imaging analysis showing occlusion of the left anterior descending coronary artery at its origin and successful stent placement, what is the most likely diagnosis for this patient's chest pain?
Question:
Reasoning:
Let's think about how to solve this problem elearly and reasonably step by step. RАы Step I: Analyze the patient's presentation and demographics. The patient is a 65-year-old male presenting with chest pain. Chest pain in this age group and gender raises high suspicion for cardiovascular pathology, particularly myocardial infarction or ischemia.
### Step 2: Evaluate the findings from the coronary angiography (Modality The angiography reveals specific abnormalities in the left coronary artery system. There occlusion, meaning complete blockage, noted at the origin of the left anterior descending (LAD) artery. Concurrently, there is mild narrowing observed in the first septal branch of the left anterior descending artery. The other major coronary arteries examined (circumflex ### Step 3: Define the condition based on the angiographic findings.AM occlusion representing complete blockage of blood flow within coronary artery is termed occlusive coronary artery disease. The location the occlusion critical determining specific type.
left main) appear normal.
### Step 4: Correlate the specific findings with known patterns of occlusive coronary artery disease. The LAD artery gives rise septal branches. An occlusion located precisely the origin of the LAD known as an occlusive seplal branch coronary occlusion (O-SBCO) or more specifically, an occlusive proximal LAD occlusion. This specific location directly explains the described Vocclusion of the left anterior descending coronary artery its origin'" and the subsequent \"mild narrowing the septal branchi".
### Step 5: Incorporate the procedural information. The presence of stent placement confirms the clinical significance of this occlusion and the diagnosis of occlusive coronary artery disease. The fact that perfusion was restored (implied by the need for stenting) explains the patient's chest pain, as the LAD supplies significant portion of the anterior myocardium.
##μ Step 6: Synthesize the findings into final diagnosis. Combining the clinical presentation (chest pain) with the specific angiographic findings (occlusion at the origin of the LAD, mild distal narrowing, successful stenting), the most accurate and specific diagnosis describing the coronary artery occlusion Occlusive Proximal LAD Occlusion, which is type of occlusive coronary artery disease.
Final Occlusive proximal left anterior descending (L.DD) occlusion.
Figure 24: Chiron-ol Reasoning on Example 5.
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Intern-to-Senior (Modality: X-ray)
Teacher, based the imaging findings of calcified focal area lateral to the greater tubercle in this 45-ycar-old male with chronic left shoulder pain, what the most likely diagnosis?
Question:
Reasoning:
Let's think about how to solve this problem clearly and reasonably step by step. Itfilt Step I: Consider the patient's chief complaint and demographic information. The patient is a 45- year-old male presenting with chronic left shoulder pain. Chronic shoulder pain age group can be caused by various conditions, including rotator cuff pathology, ostcoarthritis, or less commonly, calcific tendinitis.
### Step 2:Analyze imaging findings provided. The images reveal calcified focal area lateral the greater tubercle. This specific location is highly suggestive of calcific tendinitis, as the greater tubercle where the supraspinatus tendon attaches. Calcific tendinitis is characterized by the formation of calcium deposits within tendon, which can lead inflammation and chronic pain. itfiff Step 3:Evaluate the clinical correlation. Given the patient's age and the chronic nature of his symptoms, combined with the imaging findings of calcified tendinitis emerges as the most likely diagnosis. Other conditions such as adhesive capsulitis or degenerative changes would not typically present with a focal calcified area in this specific location. ### Step Rulule out alternative diagnoses. While conditions like ostcoarthritis rotator cuff tears can cause shoulder pain, they usually do not present with a focal calcified area on imaging. Osteoarthritis would more likely show joint space narrowing or osteophytes, and rotator cuff tears would demonstrate discontinuity retraction of the tendon, neither of which described here.
the greater tuberele, calcifie
Final answer: Calcific tendinitis of the supraspinatus tendon.
Figure 25: Chiron-ol Reasoning on Example 6.
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Reviews:
[{"review_id": "eoX9FsqsVZ", "reviewer": "Reviewer_eXAH", "summary": "Chiron-o1 is a novel approach for training VLMs to be better at medical visual and reasoning tasks. Through the use of curriculum learning, the authors first train the model to become grounded with clinical cases and their corresponding treatments (stage I). In stage II, the model is then trained with images with their corresponding diagnoses. Finally, the model is trained with their novel mentor-intern collaborative search strategy where a more powerful mentor model is generates a reasoning prefix before the an intern model completes the remaining reasoning. If the intern model reasons well and is able to complete the chain frequently to arrive at the answer, then it is clear that the approach yields a successful thought.", "questions": "1. It's still not entirely clear why you had to incorporate VQA as a separate item for fine-tuning in Equation 6. I understand that the MMRP is  not enough which is why you added VQA, but you need to further explain this point, especially since MMRP includes stage 2 which involves fine-tuning with text-image pairs. \n\n2. What is the performance of different number of intern/mentor models? Is it the case that a single intern/mentor model dominated the completion in most cases? \n\n3. Can you report how does the number of API calls/tokens change compared to rejection fine-tuning with just the intern models?", "limitations": "Yes.", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Strengths: \n\n1. Without using any reinforcement learning, the authors are able to get significant improvement with just fine-tuning data. \n\n2. The mentor-intern collaboration is a novel approach which allows for diversity in thoughts and thinking by having multiple mentor and intern models attempt to complete the question. \n\n3. Approach has been thoroughly benchmarked with general medical VQA and VQA requiring extensive reasoning, which demonstrates that the approach has a lot of merit in both simple and complex scenarios. \n\nWeaknesses: \n\n1. While the authors demonstrate that their model is better than other medical reasoning approaches, comparison against other tree-branching approaches would better help resolve the novelty of this approach. \n\n2. DeepSeek-V3 is the only model being used for validation for the various reasoning chains, but other models should be used too to prevent biases. \n\n3. Intermediate reasoning steps are not independently verified, they are only assessed based on if the final answer is correct or not.", "was_revised": true, "final_justification": "I will give it a 4. The authors did a good job with maintaining coming up with a novel strategy for training VLM for medical reasoning without excessive dependency on RL and I think people should look at this approach to expand on it for other reasoning cases.", "rebuttal": "Thank you for your valuable feedback. We’re glad the reviewer finds Chiron-o1’s curriculum learning and mentor-intern strategy “novel” and appreciates its thorough benchmarking, showing “merit in both simple and complex scenarios.” Please see our response to the reviewer’s concerns below.\n\n**Re weakness 1:** Thank you for raising this insightful question. Our proposed MICS framework leverages the collaborative interaction between the mentor model and the intern model to construct high-quality multimodal Chain-of-Thought (CoT) medical reasoning data. In essence, it employs a tree search strategy, distinct from BFS or DFS, by using the MICS-Score as an evaluation metric to guide the expansion of the CoT data construction process. To further validate the effectiveness of MICS, we compared it with other tree-branch-related CoT construction method, such as Mulberry [1]. We randomly sampled 500 instances and constructed CoT data using both MICS and Mulberry. The quality of the resulting CoT data was then evaluated using the MICS-Score. The results, as shown in the table, demonstrate that MICS outperforms other methods in generating high-quality CoT data.\n\n| Methods   | MICS-Score |\n|:---------:|:----------:|\n| Mulberry  | 66.7       |\n| MICS      | 83.2         |\n1. Yao, Huanjin, et al. \"Mulberry: Empowering mllm with o1-like reasoning and reflection via collective monte carlo tree search.\" arXiv preprint arXiv:2412.18319 (2024).\n\n**Re weakness 2:** Thank you for raising this point. We would like to kindly clarify that DeepSeek-V3 is used to compare the outputs of the intern model with the ground truth to evaluate the quality of the reasoning paths. We fully agree with your suggestion to use multiple models to avoid bias. We randomly sampled 500 instances and evaluated them using different evaluation models. As shown in the table below, the variance in MICS-Score calculated across different models is relatively small, indicating no significant bias.\n\n| Models           | MICS  | Mulberry |\n|:----------------:|:-----:|:--------:|\n| GPT-4o           | 83.7  | 67.1     |\n| Gemini 2.5 Pro   | 83.1  | 66.8     |\n| DeepSeek-V3      | 82.7  | 66.4     |\n| **(variance)**   | 0.253 | 0.123    |\n\n**Re weakness 3:** Great question. We would like to elaborate on the evaluation of intermediate reasoning steps from two perspectives: **(a) CoT Construction:** The real-world clinical cases we currently collected include only the question, ground truth, and key frames of relevant medical imaging. The absence of intermediate reasoning processes makes direct supervision of the generated reasoning steps infeasible, a common challenge in the CoT field. Most methods rely on distilling closed-source models to extract reasoning processes for constructing CoT data. Building on this, we propose MICS, which searches for high-quality CoT data through a collaborative “mentor generation” and “intern validation” approach to ensure effective supervision of intermediate reasoning steps. **(b) Quality Evaluation:** The MICS-Score serves as a universal metric for assessing the quality of reasoning paths. Specifically, an effective and logically clear reasoning path should guide a smaller model (intern) to complete subsequent reasoning and arrive at the correct answer. Thus, we indirectly validate intermediate reasoning steps by verifying whether the intern model can be inspired by the existing reasoning process, addressing the challenge of evaluating reasoning processes.\n\n**Re question 1:** Thank you for your insightful comment. The Part 2 of the MMRP dataset consists of real clinical imaging interpretation data we collected, specifically in the form of image-caption pairs. Our goal was to incorporate more image-text alignment data during the training of Chiron-o1 to enhance the model’s imaging interpretation capabilities which are different from VQA capabilities. In fact, such imaging data directly interpreted by clinicians is relatively scarce. Therefore, we also gathered visual question-answering (VQA) data from various sources to improve the model’s performance and lay a foundation for enhancing its multimodal reasoning capabilities in the future.\n\n**Re question 2.1:** We appreciate your valuable suggestion. We randomly sampled 300 data instances and conducted comparative experiments by varying the number of mentor models (1 to 4) and intern models (2, 4, 6, 8) to assess their impact. The quality of the constructed CoT data under different settings was evaluated using the MICS-Score. From the experimental results shown in the table below, it is evident that the number of mentor models significantly influences the quality of the generated reasoning paths. A larger number of mentor models enhances the diversity of the MICS search process, thereby improving the effectiveness of the reasoning data. To balance API call costs, computational resources, and performance improvements, we adopted a standard MICS configuration with three mentor models and six intern models.\n\n|          | 1 mentor | 2 mentors | 3 mentors | 4 mentors |\n|:--------:|:--------:|:---------:|:----------:|:-----------:|\n| **2 interns**   | 85.8     | 88.0      | 90.0       | 91.0        |\n| **4 interns**  | 86.0     | 88.9      | 90.5       | 90.7        |\n| **6 interns** | 85.7     | 88.5      | 91.1       | 91.3        |\n| **8 interns**| 86.4     | 89.0      | 91.0       | 91.5        |\n\n**Re question 2.2:** Thank you for your thoughtful observation. We also noted this concern during the experiments and took measures to ensure that the constructed CoT data did not predominantly originate from a single mentor model. Therefore, we tracked the participation frequency of each mentor model during the construction of the MMRP Part 3 dataset. As shown in the table, the invocation counts across different mentor models are balanced, with no significant bias toward any single model. Additionally, we would like to kindly clarify that all intern models are fully involved in evaluating the generated reasoning paths, ensuring that there is no scenario where only a subset of intern models participates in the evaluation while others do not.\n\n|                      | ChatGPT-4o | Gemini 2.5 Pro Preview | Qwen2.5-VL-72B-Instruct |\n|:--------------------:|:----------:|:----------------------:|:-----------------------:|\n| Proportion (%)   | 34.4       | 33.4                   | 32.2                    |\n\n**Re question 3:** Thank you for pointing this out. During the construction of CoT data with MICS, all intern models (open-source) were deployed locally, and only the mentor models required API calls. Taking the experiments in “Re question 2.1” as an example, we set mentor_1, mentor_2, mentor_3, and mentor_4 as ChatGPT-4o, Gemini 2.5 Pro Preview, Qwen2.5-VL-72B-Instruct, and Qwen2-VL-72B-Instruct, respectively. The API call counts and associated costs are presented in the table below. In summary, while a greater number of mentor models enhances the quality of the generated CoT data, it inevitably increases resource consumption and costs. We aim to find an optimal balance to maximize the effectiveness of MICS.\n\n|                     | 1 mentor | 2 mentors | 3 mentors | 4 mentors |\n|:-------------------:|:--------:|:---------:|:----------:|:-----------:|\n| API_call        | 317      | 790       | 1,304      | 1,973       |\n| API_cost ($)    | 3.6      | 6.4       | 7.5        | 9.0         |"}, {"review_id": "QhxoFwyFfJ", "reviewer": "Reviewer_mcTK", "summary": "This paper introduces Chiron-o1, a multimodal large language model for medical reasoning, along with a novel training methodology called Mentor-Intern Collaborative Search (MICS).\nKey Contributions:\n1. MICS Strategy: A collaborative framework where \"mentor\" models (GPT-4o, Gemini) generate reasoning steps while \"intern\" models (smaller open-source models) evaluate their quality. The system iteratively searches for effective reasoning paths by having interns complete reasoning from mentor-generated steps and selecting paths based on success rates.\n2. MMRP Dataset: A three-part multimodal medical dataset containing:\n57,630 filtered, text-only QA for basic knowledge\n5,878  image-text alignment pairs \n8,328 complex reasoning scenarios across clinical contexts\n3. Chiron-o1 Model: Trained using 3-stage curriculum learning (text QA ->  image-text alignment -> complex reasoning with MICS-generated data), achieving good performance on medical VQA and reasoning benchmarks.\nThe core innovation is MICS, which creates high-quality medical reasoning data through collaborative model evaluation rather than single-model generation or expensive human annotation. This enables more rigorous reasoning paths that significantly improve performance on complex clinical scenarios compared to existing medical MLLMs.", "questions": "1. Computational Costs of MICS\nQuestion: Can you clarify the computational resources, API costs, and environmental impact for training Chiron-o1 with MICS? How many API calls to GPT-4o/Gemini were used, and what's the approximate cost per reasoning path generated? Could you achieve similar results using only open-source models?\n\nEvaluation criteria: Clearly describing computational costs and considering cheaper alternatives would improve the paper's practical value.\n\n2. Medical Bias and Safety\nQuestion: How do you ensure MICS doesn't increase biases or mistakes from the original medical data? Did you check if the mentor-intern method introduces systematic biases? Could you share details about dataset demographics, representation of diseases across different groups (e.g., malaria, sickle cell, emerging diseases), and methods for identifying or preventing bias? Did medical experts help you assess bias risks?\n\nEvaluation criteria: Clearly discussing bias risks and showing concrete steps to reduce them would make the approach safer and more trustworthy.\n\n3. Clinical Validation and Next Steps\nQuestion: What are your plans for clinical validation? Did medical professionals help confirm the clinical usefulness of the reasoning patterns generated? Can you outline specific next steps required for clinical evaluation?\n\nEvaluation criteria: Clearly outlining plans for clinical validation and showing involvement from medical professionals would strengthen confidence in real-world applicability.\n\n4. Comparison with Human Medical Reasoning\nQuestion: How does Chiron-o1’s reasoning quality compare to human medical experts? Can you highlight areas where the model performs particularly well and situations where human oversight is still necessary?\nEvaluation criteria: Provide insights from subject matter experts to support comparisons and strengthen your conclusions.", "limitations": "Technical Limitations\nCould discuss computational costs and API expenses required, which could limit accessibility\nHeavy reliance on closed-source models (GPT-4o, Gemini) creates vulnerability to API changes, pricing modifications, or service discontinuation.\nLimited discussion of whether BERT-Score and MICS-Score truly capture medical reasoning quality, or potential biases in the evaluation framework. Bringing in expert opinion could help as well.\nGeneralization boundaries: Insufficient analysis of when/where the approach might fail or perform poorly (ie rare diseases (though examples quite rare), emerging diseases, different medical systems).\nMissing Societal Impact Discussion\nCould discuss accountability when AI-generated reasoning leads to incorrect medical decisions or diagnoses.\nEnvironmental impact: The computational intensity of MICS, requiring multiple API calls to large models and extensive inference, contributes to climate and environmental risks.\nBias amplification and error reproduction: The training data from existing sources may perpetuate existing medical biases, diagnostic errors, and systematic mistakes already present in medicine.\nHealthcare worker deskilling and automation bias: Risk that medical professionals may become overly dependent on AI reasoning, potentially diminishing critical thinking skills and leading to automation bias where clinicians uncritically accept AI-generated conclusions.\nWould help to discuss regulatory hurdles expected for clinical deployment\n\nMissing Safeguards Discussion\nClinical validation: Need further clinical evaluation before deployment, could discuss\nHuman oversight mechanisms: How to ensure appropriate human supervision\nBias monitoring: Strategies for detecting and mitigating biased reasoning patterns\nFailure mode detection: Methods for identifying when the system is operating outside its capacity", "rating": 5, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 2, "strengths_and_weaknesses": "1. Strengths\n\na. Quality\nDesign: The paper conducts comprehensive evaluations across 7 benchmarks, including both in-domain (MMRP) and out-of-domain (MedXpertQA_MM) scenarios, demonstrating good generalization capabilities.\nAblation studies: Systematic analysis of MICS components, training strategies, and dataset configurations supports design.\nMethodology: The framework here addresses a real problem in medical CoT data generation - the lack of quality evaluation mechanisms.\nConsistent improvements: Chiron-o1 achieves gains across multiple benchmarks compared to baseline models.\n\nb. Clarity\nWell-structured presentation: The paper flows well.\nClear technical exposition: The MICS algorithm is explained well and visualizations help provide more clarity.\n\nc. Significance\nAddresses healthcare need: Medical reasoning is a complicated process, not always captured in many models; improved AI capabilities could have substantial impact.\nScalable approach: MICS provides a framework for generating high-quality reasoning data that could be applied beyond medical domains.\nClear Results: Demonstrates clear advancement over existing medical MLLMs, particularly in reasoning tasks.\n\nd. Originality\nNew collaborative search approach: The mentor-intern framework for reasoning path evaluation is creative and well-motivated by educational analogies.\nEvaluation metric: MICS-Score provides a set way to assess reasoning path quality using multiple model perspectives.\nComprehensive dataset construction: The three-stage MMRP dataset design with medical content could help with other models\n\n2. Weaknesses\n\na. Quality\nThe MMRP dataset would benefit from clinician sampling and testing. There is the risk of data not being as representative or as clinically logical as expected.\nLimited theoretical analysis: The paper lacks theoretical justification for why the mentor-intern collaboration should work better than alternatives\nComputational cost concerns: MICS requires multiple API calls to expensive models (GPT-4o, Gemini) and numerous intern model evaluations, making it potentially too costly in multiple respects for large-scale deployment. The expense should be recognized to include environmental cost, as these models risk using high amounts of electricity, water and  furthering climate change and the benefit may not be as clear when paired with downstream effects. Showing what the relative costs and benefits may be, even if very rough, would help.\nEvaluation limitations:\nTest size for benchmarks\nBERT-Score for semantic similarity may not capture medical reasoning quality adequately\n\nb. Clarity\nComplex methodology: While well-explained, the MICS algorithm involves many components that may be difficult to reproduce reliably.\n\nc. Significance\nIncremental: While effective, the core contribution builds incrementally on existing CoT and collaborative reasoning approaches rather than being an entirely new approach.\nComparison limitations: Could compare with more recent medical reasoning frameworks\n\nd. Originality\nLimited novelty of each piece: Novelty ia from the combination of study parts\nDataset overlap concerns: The MMRP dataset is built on existing medical case data, often used in such studies", "was_revised": true, "final_justification": "Chiron-o1 combines curriculum learning with a mentor–intern framework for medical visual reasoning, achieving solid gains without the use of reinforcement learning (RL). The multi-mentor/intern approach introduces diversity in reasoning paths and has been thoroughly tested. In response to request for comparisons to other tree-based methods, evaluations using multiple models to reduce potential bias, and a clearer explanation of how intermediate reasoning steps are assessed, the authors provided additional comparisons, multi-model evaluations, and a detailed explanation of how their MICS framework maintains reasoning quality. Overall, the work is quite readable, introduces new approaches, and should be accepted.", "rebuttal": "Thank you for your insightful review. We’re delighted that the reviewer finds the MICS strategy “creative and well-motivated,” praises the “comprehensive” evaluations and “clear” presentation. We have summarized and organized your comments and suggestions, and we provide detailed responses below.\n\n**Re bias and safety of MMRP (weakness 1 + question 2 + limitation 6):**  We appreciate your insightful comments. While our MMRP dataset, sourced from real Radiopaedia clinical cases, is valuable, we acknowledge the potential for errors or biases from manual annotation. To address this, we implemented initial data preprocessing (e.g., removing unclear cases/low-resolution images). Crucially, to mitigate systematic biases introduced by MICS, we recruited three experienced radiologists to meticulously clean the constructed CoT data, removing unreasonable or clinically invaluable entries. The table below presents statistical data for the MMRP dataset, including representative diseases like neurological tumors and infectious diseases.\n| Age Range   | Count |\n|:-----------:|:-----:|\n| 0–9         | 241   |\n| 10–19       | 262   |\n| 20–29       | 358   |\n| 30–39       | 410   |\n| 40–49       | 395   |\n| 50–59       | 374   |\n| 60–69       | 346   |\n| 70–79       | 237   |\n| 80–89       | 108   |\n| Unknown     | 32   \n\n| Gender   | Count |\n|:--------:|:-----:|\n| Male     | 1,467 |\n| Female   | 1,301 |\n| Unknown  | 8     ||\n\n**Re theoretical analysis of MICS:** Thank you for this point. Unlike mainstream direct distillation, our MICS method constructs high-quality CoT data through collaborative interaction between mentor and intern models. Specifically, the more knowledgeable mentor model provides prompts (partial reasoning paths) that guide the weaker intern model to complete reasoning and achieve the correct answer, thereby demonstrating the value of the mentor's reasoning prefix—much like a teacher guiding a student. As Figure 4(a) of the manuscript illustrates, our MICS method is indeed more effective than other CoT annotation approaches.\n\n**Re computational cost of MICS (weakness 3 + question 1 + limitation 1 + limitation 5):** We appreciate your feedback. For Intern models, inference is handled by local deployment, with a single H100 GPU (80G) sufficient for eight models. Mentor model API costs, which are the primary concern, depend on the number of mentor models and search depth; as shown in Table 1 (API cost) and Table 2 (API calls), API consumption increases with more mentor models based on our analysis of 300 sampled cases. We acknowledge the significant cost-performance gap between closed-source and open-source models, with our CoT data currently relying on the superior reasoning of closed-source mentors (e.g., GPT, Gemini). Consequently, we advocate balancing MICS performance with resource usage and hope future open-sourcing of powerful models will enhance strategies like MICS. (We set mentor_1, mentor_2, mentor_3, and mentor_4 as ChatGPT-4o, Gemini 2.5 Pro Preview, Qwen2.5-VL-72B-Instruct, and Qwen2-VL-72B-Instruct, respectively.)\n\n| MICS (cost/$)     | ChatGPT-4o | Gemini 2.5 Pro Preview | Qwen2.5-VL-72B-Instruct | Qwen2-VL-72B-Instruct | Deepseek-V3 (evaluation) |\n|-------------------|------------|------------------------|-------------------------|-----------------------|--------------------------|\n| **1 mentor**        | 3.67       | -                      | -                       | -                     | 0.07                     |\n| **2 mentors**       | 4.76       | 4.11                   | -                       | -                     | 0.24                     |\n| **3 mentors**       | 5.26       | 4.78                   | 0.39                    | -                     | 0.52                     |\n| **4 mentors**       | 5.76       | 5.12                   | 0.43                    | 0.71                  | 1.07                     |\n\n| MICS (call_numbers) | ChatGPT-4o | Gemini 2.5 Pro Preview | Qwen2.5-VL-72B-Instruct | Qwen2-VL-72B-Instruct | Deepseek-V3 (evaluation) |\n|---------------------|------------|------------------------|-------------------------|-----------------------|--------------------------|\n| **1 mentor**          | 317        | -                      | -                       | -                     | 1,159                    |\n| **2 mentors**         | 409        | 381                    | -                       | -                     | 3,903                    |\n| **3 mentors**         | 452        | 443                    | 428                     | -                     | 8,568                    |\n| **4 mentors**         | 504        | 490                    | 487                     | 490                   | 16,753                   |\n\n**Re evaluation limitations (weakness 4 + limitation 2):** Thank you for your valuable and detailed comments. We fully agree that Bert-Score cannot fully reflect the quality of medical reasoning. Therefore, we proposed MICS-Score to address the shortcomings of existing medical reasoning evaluation metrics. Unlike other metrics, MICS-Score does not focus on character matching or semantic similarity but evaluates the mentor’s reasoning prefixes through the intern model’s performance. The advantage of this metric lies in its effectiveness in quality control (a low-quality reasoning path prompt is unlikely to guide the intern model to correct reasoning) and the robustness of the evaluation process (using multiple intern models to stabilize evaluations and avoid extreme cases). In the future, we plan to incorporate more real expert opinions as part of the evaluation metrics.\n\n**Re reproduce of MICS:** Thank you for your insightful comment. We will publicly release the code and model weights for reproducing MICS to ensure that anyone can replicate the entire process of constructing CoT data.\n\n**Re limited novel of MICS:** Thank you for your constructive critique. We would like to kindly clarify that there is currently no method using collaborative reasoning to construct high-quality CoT data, and the proposal of MICS was inspired by tree search. We aim to transfer the concept of tree search to the domain of data construction, introducing a novel approach for high-quality CoT annotation. Specifically, partial reasoning paths output by the mentor model are evaluated by the intern model to filter out superior reasoning steps, and collaborative reasoning continues along these high-quality prefixes to generate complete reasoning processes. As shown in the manuscript results, our proposed MICS effectively enhances model reasoning performance and has been recognized as a novel data construction method by Reviewers eXAH and kkRL.\n\n**Re comparison limitations:** We are thankful for your helpful suggestion. We have recently noted MLLMs related to medical reasoning. To ensure fair comparisons, we evaluated ChestX-Reasoner on five common benchmarks using the same evaluation settings. The specific results, shown in the table below, clearly demonstrate that Chiron-o1 maintains a significant advantage over the latest medical reasoning models.\n\n| Model               | vqa-rad | slake | pathvqa | pmcvqa | mmmu | Avg  |\n|-------------------|:-------:|:-----:|:-------:|:------:|:----:|:----:|\n| **ChestX-Reasoner** | 70.9    | 70.0  | 66.7    | 38.5   | 49.5 | 59.1 |\n| **Chiron-o1-2B**    | 75.4    | 85.3  | 70.3    | 54.3   | 42.1 | 65.5 |\n| **Chiron-o1-8B**    | 76.8    | 83.2  | 74.0    | 57.5   | 54.6 | 69.2 |\n\n**Re dataset overlap:** Thank you for highlighting this issue. We fully understand your concern about potential overlap between the MMRP dataset and commonly used training data or benchmarks. Unlike datasets such as MedXpertQA, PMC-VQA, and OmniMedVQA, the MMRP dataset is sourced from the Radiopaedia medical teaching website, which contains real cases uploaded by doctors from medical institutions across various countries, offering high clinical value. To date, this data has not been used as publicly available training data or benchmarks. Therefore, we confirm that the constructed MMRP dataset does not have data leakage issues.\n\n**Re clinical validation (question 3 + limitation 8):** We acknowledge your points on clinical validation, deployment, and safety. Formal clinical validation will proceed in two phases: **Systematic Retrospective Validation:** Blinded evaluation by radiologists on an independent dataset, focusing on accuracy, logical coherence, clinical utility, and safety. **Simulated Clinical Reader Studies:** Comparing doctor performance with/without Chiron-o1 to quantify improvements in accuracy, confidence, and efficiency. We will actively pursue these plans in future work.\n\n**Re generalization boundaries (question 4 + limitation 3):** Thank you for your valuable perspective. Through comprehensive qualitative analysis of existing case data, we have observed that Chiron-o1 does exhibit suboptimal generalization in certain special cases. For example, compared to human experts, the model tends to generate hallucinations when processing low-resolution images due to its inability to capture fine visual features. Additionally, as the MMRP dataset includes some rare diseases, Chiron-o1 finds it challenging to handle these cases without additional clinical information, requiring physician supervision. Honestly, we plan to address these issues in the future by further expanding the MMRP dataset to enhance the model’s reasoning performance.\n\n**Re societal impact (limitation 4 + limitation 7):** We concur on the importance of societal impact, accountability, bias, and regulation. Chiron-o1 is designed as a clinical decision support tool with verifiable reasoning to ensure human responsibility and prevent automation bias. Its interpretability also serves as an educational tool. This transparency, combined with rigorous phased validation, aims to fulfill future regulatory demands."}, {"review_id": "tseaqmpbld", "reviewer": "Reviewer_kkRL", "summary": "This paper introduces **MICS**, a novel strategy for generating a high-quality CoT dataset from VQA samples. Using MICS, the authors curate **MMRP**, a medical reasoning dataset for SFT of language models. The authors also train **Chiron-o1**, a new multimodal medical reasoning model, through a three-stage training using MMRP and evaluate it on different datasets.", "questions": "1. Could the authors clarify the versions of the baseline models used for evaluation? Specifically, the paper mentions that Gemini 2.5 Pro is used as a mentor model in the MICS process, but in the baseline comparison (e.g., Table 1), the cited paper corresponds to Gemini 1.5 Pro. Why is a stronger model used for MCIS but not included as a baseline?\n\n2. There appear to be discrepancies between the numbers reported in Table 1 and those in the original papers. For example, the LLaVA-Med paper reports accuracies of 61.52, 84.08, and 37.95 on VQA-RAD, SLAKE, and PathVQA (based on open evaluation for LLaVA-based version), whereas the numbers shown in Table 1 differ. Could the authors explain the source of these discrepancies? Were all baselines re-evaluated under a consistent setup?\n\n3. For the VQA benchmarks, what evaluation protocol was used, open-ended or multiple-choice (closed-ended) answering? This distinction significantly affects accuracy metrics and should be clarified for fair comparison.\n\n4. Have the authors explored the effect of reinforcement learning (RL) fine-tuning after the three-stage SFT process on Chiron-o1? Given the success of GRPO in Med-R1 and MedVLM-R1, it would be interesting to assess whether RL could further improve the reasoning capabilities of Chiron-o1.\n\n5. There are several issues reported regarding a lack of clear visual understanding in multimodal language models, where they often fail to capture fine-grained image details, which is a crucial ability in the medical domain. Have the authors investigated whether the MICS strategy helps mitigate this issue? Does Chiron-o1 still suffer from these limitations?\n\n6. It is not clear whether the intern models in the MICS process have access to the image input. Based on Figure 2, it appears they are only given the question and partial reasoning steps. If they do not see the image, could this harm their performance?", "limitations": "Yes.", "rating": 5, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 4, "strengths_and_weaknesses": "**Strengths**\n\n- **MICS** is a novel and well-motivated framework for generating high-quality chain-of-thought (CoT) data. It provides a generalizable method that can be applied to existing datasets to enrich them with step-by-step reasoning traces.\n- The authors introduce **MMRP**, a multimodal medical reasoning dataset, which could serve as a valuable resource for training and evaluating medical MLLMs.\n- The paper includes thorough **ablation studies** that effectively demonstrate the impact of MICS and their proposed three-stage training strategy. These analyses help validate the importance of each design choice.\n\n**Weaknesses**\n- The paper lacks clarity in some aspects of the **evaluation setup** and **implementation details**. For example, it is unclear which exact versions of baseline models (e.g., **LLaVA-Med**, **Gemini**) were used in comparison. Additionally, important training details such as the number of GPUs used, total GPU hours, and compute infrastructure are missing (information that is required by the checklist).\n- The **evaluation scope could be improved**. Since Chiron-o1 is trained on the datasets used in Table 1, it would be more convincing to include evaluations on **unseen datasets** such as *OmniMedVQA [1]* and *MediConfusion [2]*, to better assess generalization. Moreover, the absence of **proprietary models** (e.g., GPT-4o, Gemini 2.5 Pro) in Table 2 is not well justified, even though the table focuses on medical reasoning, including these models would provide useful reference points.\n- While the proposed **MICS** strategy is promising, the paper would benefit from a more detailed analysis of **failure cases** or **limitations**. For example, in what types of reasoning tasks does MICS fail to generate effective paths? Are there modalities or body systems where it underperforms?\n\n**References**  \n[1] Cheng, T., et al. *OmniMedVQA: A Benchmark for Generalized Medical Visual Question Answering*. CVPR, 2024.  \n[2] Sepehri, M. S., et al. *MediConfusion: Can you trust your AI radiologist? Probing the reliability of multimodal medical foundation models*. ICLR 2025.", "was_revised": true, "final_justification": "The discussion and additional experiments provided in the rebuttal add clarity to the paper and help strengthen the evaluation section. I believe the MICS method, MMRP dataset, and Chiron-o1 model represent valuable contributions to the field of medical multimodal reasoning. That said, the evaluation could still benefit from comparisons with more recent open-source models. Overall, despite some minor weaknesses, I find this to be a technically strong and well-executed paper.", "rebuttal": "Thank you for your insightful review. We’re delighted that the reviewer finds MICS a “novel and well-motivated framework” for generating high-quality CoT data and appreciates MMRP as a “valuable resource” for medical MLLMs, supported by “thorough ablation studies.” Please see our response to the reviewer’s concerns below.\n\n**Re weakness 1:** Thank you for highlighting this issue. In Table 1, we used LLaVA-Med-v1.5-Mistral-7B and Gemini 1.5 Pro as baseline models. During training, we fine-tuned Chiron-o1 using eight A100 GPUs (accelerated with DeepSpeed ZeRO-1), with the three training stages taking 12 hours, 12 hours, and 48 hours, respectively. More detailed supplementary information will be included in the updated manuscript.\n\n**Re weakness 2.1:** Thank you for pointing this out. Although we have already achieved SOTA performance on the out-of-domain benchmark MMMU (H&M), we conducted further tests on additional unseen benchmarks (OmniMedVQA and MediConfusion) to validate the robust generalization of Chiron-o1. \n\nAs shown in table below, Chiron-o1 demonstrates strong performance on the out-of-domain benchmark OmniMedVQA, confirming its robust VQA and reasoning capabilities.\n| Model                     | CT   | FP   | MRI  | OCT  | Der  | Mic  | X-Ray | US   | Avg. |\n|:-------------------------:|:----:|:----:|:----:|:----:|:----:|:----:|:-----:|:----:|:----:|\n| **Med-Flamingo**           | 34.6 | 33.3 | 27.5 | 26.0 | 28.3 | 28.1 | 30.1  | 33.2 | 30.2 |\n| **RadFM**                  | 33.3 | 35.0 | 22.0 | 31.3 | 36.3 | 28.0 | 31.5  | 26.1 | 30.5 |\n| **LLava-Med-7B**           | 25.3 | 48.4 | 35.9 | 42.1 | 45.2 | 44.0 | 31.7  | 83.7 | 44.5 |\n| **Qwen-VL-Chat**           | 51.5 | 45.4 | 43.9 | 54.0 | 55.4 | 49.5 | 63.1  | 33.5 | 49.5 |\n| **Yi-VL-34B**              | 39.8 | 57.2 | 51.4 | 70.5 | 54.5 | 61.4 | 40.5  | 40.5 | 54.9 |\n| **LLava-v1.6-7B**          | 40.1 | 39.5 | 54.8 | 58.4 | 54.0 | 48.8 | 53.3  | 47.9 | 49.6 |\n| **LLava-v1.6-13B**         | 40.0 | 43.6 | 47.4 | 63.2 | 58.0 | 50.5 | 59.6  | 42.6 | 50.6 |\n| **LLava-v1.6-34B**         | 50.6 | 63.4 | 60.9 | 68.4 | 65.7 | 62.8 | 74.7  | 44.5 | 61.4 |\n| **LLava-v1.5-LLaMA3-8B**   | 33.0 | 49.7 | 53.8 | 76.0 | 63.1 | 48.4 | 56.6  | 31.2 | 48.8 |\n| **HuatuoGPT-Vision-7B**    | 61.6 | 80.2 | 65.1 | 86.3 | 71.6 | 67.4 | 81.4  | 87.4 | 75.1 |\n| **HuatuoGPT-Vision-34B**   | 60.8 | 85.5 | 66.5 | 90.0 | 74.0 | 71.3 | 83.8  | 81.7 | 76.7 |\n| **Chiron-o1-2B**           | 72.0 | 81.0 | 85.5 | 84.5 | 68.5 | 73.0 | 75.5  | 57.0 | **74.6** |\n| **Chiron-o1-8B**           | 79.5 | 85.0 | 87.5 | 89.0 | 76.0 | 81.5 | 84.5  | 49.5 | **79.1** |\n\nAdditionally, we conducted tests on the intriguing MediConfusion dataset, which uses pairs of similar images sharing the same question and options to assess whether the model can accurately distinguish medical images. The results in table below indicate that Chiron-o1 outperforms most general and medical models but still lags behind large-scale closed-source models. In the future, we aim to collect more high-quality annotated real-world medical imaging data to further enhance the model’s ability to discriminate medical images.\n| Model                          | Set accuracy (%) | Confusion (%) | Individual accuracy (%) |\n|:------------------------------:|:----------------:|:-------------:|:-----------------------:|\n| **RadFM**                      | 0.57             | 97.54         | 35.90                   |\n| **LLaVA-Med-v1.5 (Mistral 7B)**| 0.00             | 100.00        | 23.58                   |\n| **Gemini 1.5 Pro**             | 19.89            | 58.52         | 51.14                   |\n| **BLIP-2 (Opt 2.7B)**          | 0.57             | 92.19         | 22.16                   |\n| **GPT-4o**                     | 18.75            | 75.00         | 56.25                   |\n| **MedFlaming**                 | 1.14             | 98.75         | 47.73                   |\n| **Claude 3 Opus**              | 8.52             | 84.09         | 50.85                   |\n| **LLaVA-v1.6 (Mistral 7B)**    | 8.52             | 85.47         | 50.57                   |\n| **Chiron-o1-8B**               | 10.22            | 84.91         | 53.73                   |\n\n**Re weakness 2.2:** Thank you for raising this important point. Similarly, we have supplemented Table 2 in the manuscript by including experimental results for some proprietary models. The additional results show that Chiron-o1-8B can achieve performance comparable to large-scale closed-source models on reasoning-focused benchmarks. In the future, we aim to further refine MICS to collect higher-quality CoT data, thereby surpassing current performance levels.\n\n\n\n| Model       | Text  | MXQA-R | MXQA-U | R-ACC | R-Bert | R-MICS |\n|-----------|:-----:|:------:|:------:|:-----:|:------:|:------:|\n| **GPT-4o**        | 92.4  | 30.0   | 27.2   | 55.8  | 85.8   | 46.5   |\n| **Gemini 2.5 Pro**| 90.8  | 29.5   | 25.8   | 54.6  | 86.0   | 45.7   |\n| **Chiron-o1-8B**  | 92.1  | 23.3   | 25.1   | 58.4  | 90.4   | 49.4   |\n\n> **Abbreviations**:\n> - `Text`: MMRP (Pure Text)\n> - `MXQA-R`: MedXpertQA-MM (Reasoning)\n> - `MXQA-U`: MedXpertQA-MM (Understanding)\n> - `R-ACC`, `R-Bert`, `R-MICS`: MMRP (Reasoning) with ACC, Bert-Score, and MICS-Score respectively\n\n**Re weakness 3:** Thank you for your valuable perspective. Following your insightful suggestion, we conducted a thorough review of the constructed CoT data and identified several cases where reasoning failed. Through comprehensive analysis, we summarized several key issues: (a) When the resolution of the associated images is low, the model struggles to interpret them adequately, leading to the failure of MICS. (b) Since the collected data are real clinical cases uploaded by doctors from various hospitals on the Radiopaedia website, we found that a small number of cases contain discrepancies between the annotation information and the content displayed in the images (possibly due to clinicians’ errors). This also hinders MICS from effectively searching for high-quality CoT data. Furthermore, based on the evaluation results on OmniMedVQA from “Re weakness 2.1,” the scarcity of collected ultrasound modality data likely contributes to the model’s performance decline. Moving forward, we plan to collect more real-world clinical cases to expand the MMRP dataset, thereby addressing the aforementioned issues.\n\n**Re question 1:** Thank you for your critical and insightful feedback. We will include the versions of the baseline models in the updated manuscript, as mentioned in “Re weakness 1.” Additionally, due to our oversight, we did not initially include the more powerful closed-source model Gemini 2.5 Pro as a baseline for comparison. We have conducted further comparative experiments, with the results presented below:\n\n| Model             | vqa-rad | slake | pathvqa | pmcvqa | mmmu | Avg  |\n|:-----------------:|:-------:|:-----:|:-------:|:------:|:----:|:----:|\n| **Gemini 1.5 Pro**| 60.3    | 72.6  | 70.3    | 52.3   | 47.9 | 60.7 |\n| **Gemini 2.5 Pro**| 71.3    | 80.5  | 74.1    | 61.1   | 57.1 | 68.8 |\n| **Chiron-o1-8B**  | 76.8    | 83.2  | 74.0    | 57.5   | 54.6 | 69.2 |\n\n**Re question 2 and 3:** Thank you for your constructive critique. Regarding the experimental results in Table 1 of the manuscript, we would like to kindly clarify that we adopted the test set splitting standard from HuatuoGPT-Vision, which differs from that of LLaVA-Med. Ultimately, our experimental results are consistent with those reported in HuatuoGPT-Vision. To ensure fairness in the comparison, we adopted the evaluation settings of HuatuoGPT-Vision (whose code is open-source) when testing Chiron-o1, InternVL3, and other models. All experimental results in Table 1 were evaluated using a unified standard for close-ended questions.\n\n**Re question 4:** Thank you for your suggestion, which strengthens our work. As you noted, reinforcement learning (RL) methods such as GRPO, PPO, RLOO, and REINFORCE++ have already shown significant impact in the field of medical reasoning. Given that Chiron-o1 has developed robust reasoning capabilities through multi-stage training, we fully agree that using it as a baseline and applying RL algorithms could potentially unlock even more robust and powerful reasoning abilities. This is already part of our future work plan, and we look forward to sharing updates in next version of Chiron-o1.\n\n**Re question 5:** Thank you for your valuable perspective. We had indeed taken note of the issue you raised during the data collection process. Given that fine-grained visual understanding is critical for MLLMs to address various tasks, particularly reasoning tasks, we prioritized collecting data annotated by clinicians to include key diagnostic information (such as illustrated interpretations and case discussions) that align closely with the detailed features presented in the images. Through the construction of reasoning data using MICS and multi-stage training, we observed improvements in the model’s visual understanding, as evidenced by the experimental results and case studies described in the manuscript. However, we acknowledge that these limitations have not been fully overcome, a challenge also faced by general MLLMs that remains to be addressed. In the future, we plan to tackle this issue by expanding the collection of high-quality CoT data and refining our data construction and model training strategies.\n\n**Re question 6:** Thank you for your kind reminds. In fact, the intern model does have access to images. We will revise Figure 2 in the updated manuscript to avoid the misunderstanding."}, {"review_id": "e9TREhuC8G", "reviewer": "Reviewer_kRLm", "summary": "Chain-of-Thought (CoT) data is crucial for enhancing the reasoning capabilities of medical multi-modal large language models (MLLMs). The authors propose a Mentor-Intern Collaborative Search (MICS) method, which generates optimal CoT data by evaluating the scores of different reasoning paths. Based on this approach, they construct the MMRP dataset and train the Chiron-o1 model, which achieves outstanding performance on multiple VQA and reasoning benchmarks.", "questions": "1. Is it possible for the dataset constructed in this paper to be open-sourced? Making it publicly available would be a valuable contribution to the entire research community.\n2. Medical imaging is an inherently rigorous discipline, and the capabilities of general-purpose MLLMs are limited in medical contexts. While optimizing the MLLM output distribution using certain metrics (such as the MICS-score) can to some extent improve the quality of generated CoT data, can CoT data generated without leveraging dedicated medical knowledge or tools like knowledge graphs truly be considered reliable? How can the correctness of the underlying medical logic be ensured? I hope the authors can provide a response on this issue.\n3. The authors need to demonstrate that the proposed MLLM does in fact incorporate imaging information in its output, rather than relying solely on prompts and textual knowledge the model has acquired.", "limitations": "yes", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Strengths: \n1. The experiments in the paper are comprehensive, the structure is clear, and the writing is well-organized.\n2. The Mentor-Intern Collaborative Search (MICS) strategy is straightforward and plays an important role in improving the quality of CoT data.\n\nWeaknesses:\n1. MICS relies on the intern's feedback to refine the CoT paths generated by the mentor; however, the method essentially depends on the mentor model's capability to produce CoT data. Is the mentor model itself sufficiently competent to generate reliable and high-quality reasoning data? This point warrants further discussion.\n2. The paper presents several examples (e.g., Figure 3, Figure 20) where the prompts provide extensive sign-related information, which is almost equivalent to revealing the answer. The reviewer is unsure whether the model is genuinely leveraging image features, or is simply generating responses based on the content of the prompt. The authors should validate this.\n3. In many real clinical scenarios, such as medical screening or physical examinations, there is often no sign-specific prompt and only imaging data is available—the model needs to generate a diagnostic report. How does the model perform under such conditions?\n4. If the prompt contains incorrect sign/disease-related information (e.g., the patient reports abdominal pain and is assumed to have appendicitis, but actually has a bowel obstruction), will the model be misled and provide suggestions along the wrong path? The authors should investigate and verify this.", "was_revised": true, "final_justification": "The additional discussion and experiments have largely addressed my main concerns. I encourage the authors to further clarify these points in the final version. Hence, I recommend accepting this paper.", "rebuttal": "Thank you for your thoughtful review. We’re pleased that the reviewer finds the Mentor-Intern Collaborative Search (MICS) strategy “straightforward” and impactful for improving CoT data quality, and appreciates the “comprehensive” experiments and “well-organized” writing. Please see our response to the reviewer’s concerns below.\n\n**Re weakness 1:** Thank you for your valuable and detailed comments. Currently, the majority of CoT data construction methods are derived through distillation from closed-source models. We would like to kindly clarify that using large-scale models as mentor models to construct reasoning paths is a well-established approach. Building on this, our proposed MICS effectively enables collaborative search between mentor and intern models to ensure CoT quality. To ensure the reliability and validity of the MMRP_Part3 dataset, we recruited three doctoral students with practical clinical experience in radiology to manually review and validate the data, ultimately filtering out dozens of low-quality or non-practical samples. As evident from Table 1, Table 2, and the case studies in the manuscript, post-training with MICS-constructed data significantly enhances Chiron-o1’s VQA and reasoning capabilities. Furthermore, we tested the direct distillation CoT construction method against MICS using MICS-Score. The results, which show a gradual increase in MICS-Score as reasoning steps deepen, suggest that our approach is effective. The results presented in the table below demonstrate that direct distillation using the mentor model (“vanilla”) can yield a certain proportion of high-quality CoT data, and employing MICS further increases this proportion.\n\n| Methods   | valid (%) | non-increasing (%) | constant (%) | fluctuating (%) |\n|:---------:|:---------:|:------------------:|:------------:|:---------------:|\n| **vanilla** | 65.0      | 8.3                | 14.7         | 12.0            |\n| **MICS**    | 74.7      | 4.0                | 12.3         | 9.0             |\n\n**Re weakness 2:** Thank you for your helpful comment on this point. In fact, the metadata used in MMRP originates from real clinical cases uploaded by multiple medical centers, enriched with detailed clinical annotations (e.g., imaging interpretations and case discussions). We would like to kindly clarify that we only utilized this additional information to construct high-quality QA and CoT, aiming to minimize hallucinations. During the inference stage, some information (e.g., detailed imaging findings) is provided as input, but this is insufficient to reveal the final answer and is intended solely to assist the model in interpreting images. For instance, the “abnormal flattening of the left parieto-occipital bones” mentioned in Figure 3 does not equate to the ground truth “Primary congenital plagiocephaly.” To further validate whether the model genuinely leverages image features, we conducted experiments on the case in Figure 3, allowing the model to reason with and without images. In the latter case, the model produced an incorrect answer, “Flattened parieto-occipital bones, a normal anatomical variant,” and introduced more hallucinations due to the absence of imaging data. In the revised manuscript, we will include the reasoning processes for both scenarios to provide readers with a clearer understanding of this point.\n\n**Re weakness 3:** Thank you for your suggestion to refine this aspect. The report generation tasks you mentioned (e.g., physical examination reports, chest X-ray reports) are indeed common in real-world medical scenarios. Honestly, although our training data did not include this type of multimodal data, we believe Chiron-o1 can effectively generalize to out-of-domain tasks. Specifically, we constructed a dataset based on MIMIC-CXR for chest X-ray report generation, encompassing two tasks: finding generation and impression generation. We then evaluated Chiron-o1’s report generation performance using BERTScore, BLEU, ROUGE-1, ROUGE-2, and ROUGE-L as metrics. The evaluation results for these two tasks are presented in Tables 1 and 2 below. It is evident that, compared to general large-scale MLLMs and foundation models specialized for chest X-rays, Chiron-o1 achieves comparable performance.\n\n|     Models (Finding Generation)             | BERTScore | BLEU  | ROUGE-1 | ROUGE-2 | ROUGE-L |\n|-------------------------|:---------:|:-----:|:-------:|:-------:|:-------:|\n| **HuatuoGPT-Vision-7B**   | 85.4      | 9.7   | 27.8    | 5.3     | 16.1    |\n| **Qwen2.5-VL-72B-Instruct**| 84.6      | 5.6   | 22.7    | 4.2     | 14.2    |\n| **CheXagent**             | 74.6      | 0.3   | 4.7     | 0.0     | 4.3     |\n| **MedVLM-R1**             | 82.1      | 5.9   | 16.8    | 2.7     | 12.1    |\n| **ChestX-Reasoner**       | 82.2      | 4.8   | 14.4    | 4.1     | 11.5    |\n| **Qwen2.5-VL-7B-Instruct**| 84.5      | 7.2   | 22.4    | 4.2     | 14.0    |\n| **GPT-4o**                | 86.2      | 11.1  | 30.4    | 6.4     | 18.4    |\n| **GPT-4o-mini**           | 85.2      | 6.7   | 22.8    | 3.8     | 14.2    |\n| **DeepSeek-VL2**          | 82.3      | 4.4   | 14.9    | 3.0     | 10.6    |\n| **Chiron-o1-2B**                    | 86.1      | 8.7   | 25.6    | 6.4     | 17.4    |\n| **Chiron-o1-8B**                    | 86.2      | 10.7  | 26.7    | 7.4     | 17.8    |\n\n| Models (Impression Generation)    | BERTScore | BLEU  | ROUGE-1 | ROUGE-2 | ROUGE-L |\n|-----------------------------------|:---------:|:-----:|:-------:|:-------:|:-------:|\n| **HuatuoGPT-Vision-7B**           | 83.5      | 2.8   | 10.6    | 1.8     | 7.5     |\n| **Qwen2.5-VL-72B-Instruct**       | 83.3      | 2.8   | 10.2    | 2.2     | 6.9     |\n| **CheXagent**                     | 81.4      | 7.3   | 9.8     | 0.0     | 9.8     |\n| **MedVLM-R1**                     | 81.5      | 1.9   | 6.1     | 0.5     | 4.8     |\n| **ChestX-Reasoner**               | 83.6      | 4.5   | 12.3    | 4.0     | 10.8    |\n| **Qwen2.5-VL-7B-Instruct**        | 83.6      | 3.7   | 10.0    | 2.0     | 7.2     |\n| **GPT-4o**                        | 84.5      | 4.1   | 13.9    | 3.0     | 9.8     |\n| **GPT-4o-mini**                   | 83.7      | 2.5   | 9.7     | 1.4     | 6.6     |\n| **DeepSeek-VL2**                  | 82.4      | 2.6   | 7.1     | 0.9     | 6.3     |\n| **Chiron-o1-2B**                  | 83.9      | 3.2   | 9.5     | 2.0     | 7.8     |\n| **Chiron-o1-8B**                  | 83.8      | 3.7   | 10.3    | 2.0     | 8.1     |\n\n**Re weakness 4:** Thank you for your constructive critique. We find the reviewer’s suggestion highly interesting and worthy of exploration. After validating multiple cases, experimental results consistently demonstrate that incorrect guidance or prompts are insufficient to lead Chiron-o1 to generate unreasonable reasoning processes. Here, we present an example with the question: “Teacher, based on the imaging findings of dilated small and large bowel with a transition point at the splenic flexure showing an ‘apple core’ lesion, what is the most likely diagnosis causing this patient’s large bowel obstruction? (Supplementary information is as follows: Gender: Male, Age: 40 years, Chief complaint: Abdominal bloating.)” The ground truth is “obstructing splenic flexure colon cancer.” Due to the constraints of the rebuttal response, we are unable to display the image here. If we intentionally provide an incorrect prompt in the question, such as “suggestive of bowel obstruction caused by colonic lymphoma,” Chiron-o1 can still filter out the erroneous information and arrive at the final diagnosis of “splenic flexure colon carcinoma,” which is consistent with the ground truth. Therefore, we have reason to believe that Chiron-o1 is capable of resisting interference from incorrect information. The detailed reasoning process and specifics will be included in the revised manuscript.\n\n**Re question 1:** Thank you for your attention. We will make the code, model, and data for reproducing MICS publicly available.\n\n**Re question 2:** We are grateful for your constructive feedback. Your perspective on this issue aligns closely with our own. Indeed, medicine is a highly rigorous discipline, and methods lacking medical logic are unacceptable, as they are irresponsible to human health. We would like to kindly clarify that the process of constructing CoT data with MICS is supervised and informed by real clinical data. Specifically, all case data from Radiopaedia are raw data verified by clinicians from medical centers across various countries and deemed valuable for learning. We collected information such as indications, age, gender, imaging interpretations, abnormal target localization, and case discussions to ensure the reliability of the MMRP dataset constructed with MICS. To further ensure the clinical logic of the data, we recruited three experienced radiology doctoral students to clean the MMRP data, retaining only high-quality CoT data. Additionally, we have noted related work on using knowledge graphs to generate CoT (e.g., MedReason). However, such work focuses solely on the text modality. In the future, we plan to adapt the paradigm of using knowledge graphs to construct CoT for multimodal medical reasoning.\n\n**Re question 3:** Thank you for highlighting this issue.  Please see our detailed answer to this question in response to weakness 2."}, {"review_id": "OfldOEZ5zD", "reviewer": "Reviewer_TAs2", "summary": "This paper proposes a novel approach for searching and evaluating effective reasoning paths towards critical diagnosis in the medical domain. The authors construct a multi-task medical reasoning dataset with ranked difficulty and a new medical MLLM devised via a curriculum learning strategy. Extensive experiments demonstrate that Chiron-o1 achieves state-of-the-art performance across a list of medical visual question answering and reasoning benchmarks.", "questions": "I wonder if the multi-stage learning paradigm is generalizable across different MLLMs. The ablations about this is only conducted on MMMU with Chiron-o1-8B.", "limitations": "Yes", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 2, "originality": 2, "strengths_and_weaknesses": "Strengths:\n\n- A multimodal medical dataset comprising QA pairs is introduced, which can contribute to the medical community.\n- The authors propose a new multimodal medical model that demonstrates outstanding reasoning abilities in handling both in-domain and out-of-domain complex clinical problems.\n- The proposed approach achieves competitive performance compared to previous SOTA medical MLLMs through extensive experiments across multiple benchmarks.\n\nWeaknesses:\n\n- The proposed medical dataset is basically a reorganization of existing medical datasets. There is no new high-quality dataset annotated in this paper. The contribution is limited.\n- As shown in Figure 5, the dataset is imbalanced across different body systems.\n- The source and resolution of the images in datasets are different and imbalanced. Also there is no high-resolution images, limiting the usefulness of the proposed dataset\n- The novelty of the Multi-Stage SFT is limited. It is basically the curriculum learning pattern. There is also no ablation and experiment results supporting that it is better than every single single-stage learning.\n- More qualitative results is needed for different test set.", "was_revised": true, "final_justification": "The clarifications regarding the dataset construction, image resolution, and the design rationale behind the multi-stage training strategy somewhat addressed my concerns. The additional experimental results are helpful. Considering the data is not from real clinical cases and I already gave a positive rating, I will keep my original scores.", "rebuttal": "Thank you for your valuable feedback. We’re thrilled that the reviewer recognizes the “outstanding reasoning abilities” of our new multimodal medical model and the “competitive performance” of our approach, as well as the contribution of our multimodal medical dataset. Please see our response to the reviewer’s concerns below.\n\n**Re weakness 1:** Thank you for your concern. We would like to kindly clarify that the metadata for the MMRP dataset is sourced from the Radiopaedia public teaching website, and extracting valuable real clinical case data from it is a labor-intensive process. Based on the collected data, we proposed a novel CoT data construction method, MICS, as acknowledged by Reviewers eXAH and kkRL. This method enables collaborative search between mentor and intern models to identify high-quality reasoning paths, thereby completing the annotation of the reasoning process for the entire dataset. As you noted, manual data annotation is currently a time-consuming and labor-intensive task, not only in the medical field but also in other domains. Therefore, we aim to propose an automated CoT data annotation process to alleviate this issue and further enhance the performance of MLLMs in medical reasoning. To ensure that the constructed data adheres to strict clinical logic and practical value, we recruited three experienced radiologists to review and validate the MMRP dataset, retaining only high-quality CoT annotations for subsequent training.\n\n**Re weakness 2:** Thank you for your critical and insightful feedback. We had previously noted the issue you raised. The imbalanced data distribution in MMRP is inherited from the varying patient volumes across clinical departments. However, as shown in the experimental results (Table 1 and Table 2) in the manuscript, the existing data is sufficient to improve the model’s VQA and reasoning performance. Following your suggestion, we plan to expand the MMRP dataset in the future, balancing data across different body parts and focusing on collecting cases of rare diseases.\n\n**Re weakness 3:** Thank you for your constructive critique. Indeed, the images in our dataset are sourced from real data uploaded by doctors from medical centers in different countries. We believe this enhances the diversity of the MMRP dataset, thereby improving the broad applicability of MICS and the generalizability of Chiron-o1. As you pointed out, variations in resolution and data sources may introduce biases. To address this, we normalized the resolution of all images before using MICS for data construction. Additionally, our analysis shows that the maximum image resolution in the MMRP dataset is 3001 × 6229, with an average resolution of 660.5 × 676.8. The table below presents the pixel count distribution of all images in the MMRP dataset.\n\n| Resolution range (total number of pixels) | Numbers  |\n|:-----------------------------------------:|:--------:|\n| <1k                                       | 2        |\n| 1k-10k                                    | 905      |\n| 10k-50k                                   | 6,865    |\n| 50k-100k                                  | 827      |\n| 100k-200k                                 | 578      |\n| 200k-500k                                 | 536      |\n| 500k-1000k                                | 175      |\n| >1000k                                    | 49       |\n\n**Re weakness 4:** Thank you for your valuable perspective. We would like to kindly clarify that MICS, used for annotating high-quality CoT data, and MICS-Score, used as a metric to evaluate reasoning paths, are the primary contributions of this paper. Building on the MMRP dataset, Chiron-o1, trained through a three-stage SFT process, demonstrates robust reasoning performance. As you mentioned, the stage-wise training approach is essentially a form of curriculum learning. However, we designed the stages to emulate the learning logic of real medical students. Specifically, stage 1 uses a large volume of QA data to enhance the model’s ability to address text-only problems; stage 2 leverages real images and their annotations to improve image-text alignment; and stage 3 further strengthens reasoning performance using high-quality CoT data annotated by MICS. To demonstrate the advantage of this strategy over single-stage training, the results presented in the table below show that the three-stage SFT approach indeed leads to better model performance. In the future, we plan to incorporate RL algorithms (such as GRPO and PPO) into the SFT training framework to further enhance the generalizability of reasoning capabilities.\n\n| Stage | VQA-RAD | SLAKE | MMMU(H&M) | MedXpertQA_MM |\n|-----------|:-------:|:-----:|:---------:|:------------:|\n| **Stage 1** | 76.1    | 82.5  | 47.7      | 23.7         |\n| **Stage 1 + 2** | 76.1   | 82.7  | 48.4      | 23.2         |\n| **Stage 3** | 75.3    | 81.2  | 51.1      | 23.7         |\n| **Stage 1 + 2 + 3** | 76.8   | 83.2  | 54.6      | 24.2         |\n\n**Re weakness 5:** Thank you for your constructive critique. Your suggestions are highly valuable for improving the quality of our paper. Following your and other reviewers’ recommendations, we conducted further investigations into several questions, including whether MLLMs genuinely utilize image features for reasoning, the distribution of failure cases in data types constructed by MICS, and whether incorrect guidance in input information negatively impacts model reasoning. These qualitative experimental results will be included in the revised manuscript. Additionally, we plan to collect data from more diverse sources to further validate the model’s generalizability.\n\n**Re question 1:** Thank you for highlighting this issue. As shown in Figure 4(b) of the paper, we conducted ablation studies on multiple benchmarks, including VQA-RAD, SLAKE, MMMU(H&M), and MedXpertQA_MM, to demonstrate the effectiveness of multi-stage training for Chiron-o1-8B. To further verify that the multi-stage SFT paradigm can generalize to other MLLMs, we extended our investigation to Chiron-o1-2B. The specific results, shown in the table below, clearly indicate that the multi-stage training strategy also improves VQA and reasoning performance for other MLLMs. This success is attributed to the design of each stage, which is inspired by the learning process of real doctors.\n\n| Stage | VQA-RAD | SLAKE | MMMU(H&M) | MedXpertQA_MM |\n|-----------|:-------:|:-----:|:---------:|:------------:|\n| **Stage 1** | 71.3    | 81  | 37.5      | 21.2         |\n| **Stage 1 + 2** | 69.5   | 82.9  | 36      | 21         |\n| **Stage 3** | 68.8    | 71.2  | 35.5      | 21.1         |\n| **Stage 1 + 2 + 3** | 75.4   | 85.3  | 42.1      | 21.5         |\n\n**Re paper formatting concerns:** Thank you for your kind reminds. We will revise the figures in the updated manuscript."}]

Author rebuttal:

Meta-review: The authors present a study on advancing reasoning capabilities in multimodal large language models (MLLMs) for the medical domain. They suggest that lack of systematic approaches for constructing chain-of-thought (CoT) data as a key barrier to progress. To this end, they introduce Mentor-Intern Collaborative Search (MICS), a framework for generating and evaluating reasoning paths. MICS employs mentor models to guide initial reasoning steps, followed by intern models that extend these paths, with final selection based on an MICS-Score designed to assess reasoning quality. Building on this framework, the authors develop MMRP, a multi-task medical reasoning dataset with graded difficulty, and Chiron-o1, a medical MLLM trained via curriculum learning. Experimental results suggest that Chiron-o1, trained with data generated through MICS, outperforms prior models on multiple medical visual question answering and reasoning benchmarks. The work emphasizes contributions at both the methodological and resource levels, with datasets, models, and code promised for public release.

Reviewers consistently highlight several strengths: the originality of the mentor–intern paradigm, comprehensive benchmarking across multiple datasets, ablation studies validating design choices, and strong empirical performance gains without resorting to reinforcement learning. The paper is also praised for its clarity and well-structured presentation, with evaluations demonstrating both in-domain and out-of-domain generalization. Weaknesses include limited evaluation scope (initially lacking comparisons to some recent baselines), high computational costs of MICS, and concerns about the need for more rigorous clinical validation and expert involvement. Additionally, some reviewers noted incremental rather than radical novelty, missing details on evaluation protocols, and underexplored failure cases. Importantly, the authors’ rebuttal successfully addressed most concerns: they added comparisons to alternative tree-search methods and recent baselines, provided detailed computational cost analyses, demonstrated multi-model evaluations to reduce bias, and clarified intermediate reasoning assessment. They also outlined plans for clinical validation and safeguards for safe deployment. Overall, while the paper has some limitations in evaluation breadth and resource efficiency, is a nice addition to Neurips and I recommend acceptance.
Decision: Accept (poster)