PediatricsGPT: Large Language Models as Chinese Medical Assistants for Pediatric Applications
Authors: Dingkang Yang
Jinjie Wei
Dongling Xiao
Shunli Wang
Tong Wu
Gang Li
Mingcheng Li
Shuaibing Wang
Jiawei Chen
Yue Jiang
Qingyao Xu
Ke Li
Peng Zhai
Lihua Zhang
Venue: NeurIPS 2024 poster (2024)
Keywords: Large Language Models
Medical Applications
Primary area: natural_language_processing
TL;DR: This paper presents the first Chinese pediatric LLM assistant and the corresponding high-quality dataset to facilitate medical community development.

Abstract: Developing intelligent pediatric consultation systems offers promising prospects for improving diagnostic efficiency, especially in China, where healthcare resources are scarce. Despite recent advances in Large Language Models (LLMs) for Chinese medicine, their performance is sub-optimal in pediatric applications due to inadequate instruction data and vulnerable training procedures.
To address the above issues, this paper builds PedCorpus, a high-quality dataset of over 300,000 multi-task instructions from pediatric textbooks, guidelines, and knowledge graph resources to fulfil diverse diagnostic demands. Upon well-designed PedCorpus, we propose PediatricsGPT, the first Chinese pediatric LLM assistant built on a systematic and robust training pipeline.
In the continuous pre-training phase, we introduce a hybrid instruction pre-training mechanism to mitigate the internal-injected knowledge inconsistency of LLMs for medical domain adaptation. Immediately, the full-parameter Supervised Fine-Tuning (SFT) is utilized to incorporate the general medical knowledge schema into the models. After that, we devise a direct following preference optimization to enhance the generation of pediatrician-like humanistic responses. In the parameter-efficient secondary SFT phase,
a mixture of universal-specific experts strategy is presented to resolve the competency conflict between medical generalist and pediatric expertise mastery. Extensive results based on the metrics, GPT-4, and doctor evaluations on distinct downstream tasks show that PediatricsGPT consistently outperforms previous Chinese medical LLMs. The project and data will be released at https://github.com/ydk122024/PediatricsGPT.

PediatricsGPT: Large Language Models as Chinese Medical Assistants for Pediatric Applications
Dingkang Yangl.381
Jinjie Wei
Dongling Xiao²t
Shunli Wang 18
Tong Wu Yue Jiang" !8
Gang Li
Mingcheng Li Shuaibing Wang" I8
Jiawei Chen !
Qingyao Xu
Li Peng Zhai 1.3-
Lihua Zhang 133,4,5
Academy for Engineering and Technology, Fudan University, Shanghai, China Tencent Youtu Lab, Shanghai, China
9 Cognition and Intelligent Technology Laboratory, Shanghai, China
Enginecring Research Center of Al and Robotics, Ministry of Education, Shanghai, China
P AI and Unmanned Systems Engineering Research Center of Jilin Province, Changchun, China (dkyang20, pzhai, lihuazhang] @fudan. jjwei230m. fudan. edu, cn, zdluestcooutlook.c bristanli@tencen com
Abstract
Developing intelligent pediatric consultation systems offers promising prospects for improving diagnostic efficiency, especially in China, where healthcare sources are scarce. Despite recent advances in Large Language Models (LLMs) for Chinese medicine, their performance is sub-optimal pediatric applications due to inadequate instruction data and vulnerable training procedures. To ad- dress the above issues, this paper builds PedCorpus, a high-quality dataset of over 300,000 multi-task instructions from pediatric textbooks, guidelines, and knowl- edge graph resourees to fulfil diverse diagnostic demands. Upon well-designed PedCorpus, we propose PediatricsGPT, the first Chinese pediatric LLM assistant built on systematic and robust training pipeline. In the continuous pre-training phase, we introduce hybrid instruction pre-training mechanism to mitigate the internal-injected knowledge inconsistency of LLMS for medical domain adapta- tion. Immediately, the full-parameter Supervised Fine- Sine-TTning (SFT) is utilized to incorporate the general medical knowledge schema into the models. After that, we devise direct following preference optimization to enhance the generation pediatrician- humanistic responses. In the parameter- efffcccet secondary SFT phase, mixture of universal-speciffe experts strategy is presented to solve the competency conflict between medical generalist and pediatric exper- tise mastery. Extensive results based the metries, GPT-4, and doctor evalua- tions on distinct downstream tasks show that PediatricsGPT consistently outper- forms previous Chinese medical LLMs. The project and data will be released at attps://g/ thub. wwt.coo http:/ydkk22222/Peeeee atri csGPT. asG..............
1 Introduction
As an essential component of medicine, pediatrics plays an indispensable role in ensuring children's 's health growth [22, 23]. The unbalanced distribution of healthcare resources [36] has resulted in massive shortage of pediatricians, especially in populous countries led by China [37, 19]. With the
'Equal first contributions. Equal second contributions. "Corresponding authors. PProecc lead.
38th Conference on Neural Information Processing Systems (NcurIPS 2024).

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rapid advances in LLMs exemplified by ChatGPT [33], developing intelligent pediatric consultation systems provides promise for enriching medical services. Although Chinese LLMs [18, 59, 2, 57, 20] have exhibited progress in general language understanding, they are incompetent in the pediatric medical field due to the lack of domain-specifie discipline and specialized expertise injection.
To fulfil the interactive demands of Chinese medicine, preliminary efforts [8, 45, 50, 15] have enhanced LLMs' healthcare mastery through Supervised Fine-Tuning (SFT) training and medically relevant corpus collection. Despite improvements, challenges remain due to unavoidable dilemmas, including inadequate instruction data and vulnerable training procedures. Specifically, (i) existing instruction data typically involve vanilla rephrasing of the general medical corpus [50] or aggregation of doctor-like dialogues [56]. which loses the specialization and focus in pediatric applications. More importantly, the current straightforward different round instruction construction paradigms [58. 15] fail to accommodate multi-task healthcare services real-world scenarios, limiting the model generalization and inducing response hallucination. (ii) Furthermore, prior methods mostly relied on SFT to compensate for medical instruction following capabiliiiess ignoring the discrepancies between inherent and externally absorbed knowledge within the models. This single pattern causes secondary LLMs to lapse into excessive role-playing rather than understanding [40]. Despite a few attempts in the pre-training and Reinforcement Learning from Human Feedback (RLHF) phases [7, 34J, their performance is restricted by actor-critic instability [41] and online sampling bias [61].
Motivated by these observations, we construct PedCorpus, a high qualityality dataset with over 300,000 instructions across single-turn and multi-turn medical conversations. Besides containing general- ist healthcare data, PedCorpus incorporates multi-dimensional corpora from pediatric textbooks, guidelines, and knowledge graphs to ensure medical knowledge's accuracy. Vanilla instructions can also be readily extended to seed instructions for generating specialized corpora to serve different training phases. Furthermore, we integrate the well-presented GPT-4-distilles data with authentic doctor-patient dialogue data to standardize the fluency and faithfulness of instruction information.
Among our PedCorpus, we propose PediatricsGPT, the first Chinese pediatric LLM assistant with pe- diatric expertise and medical generalist. PediatriesGPT is developed on a systematic training pipeline that includes Continuous Pre-Training (CPT), full-parameter SFT, human preference alignment, and parameter-efficiee mechanism in CPT to bridge the capability weakening due to corpus format discrepancies between the internal and injected medical knowledge of foundation models, facilitating knowledge accumulation and extension. Meanwhile, a Direct Following Preference Optimization (DFPO) in human preference alignment is devised to enhance response robustness and align human preferences. Additionally, we present a mixture of universal-specific experts strategy to tackle the competency conflict between medical generalist and pediatric expertise in secondary SFT via Low-Rank Adaptation (LoRA) [27], which strengthens the model's 's adaptability to distinct downstream tasks. We conduct three pragmatic pediatric tasks to evaluate the different capabilities of existing models. Extensive experiments on pediatric and public benchmarks show that our PediatricsGPT outperforms open-source Chinese medical LLMs and baselines, yiclding competitive performance compared to GPT-3.5-turbo.
secondary SFT. In this case, we introduce hybrid instruction pre-training
2 Related Work
Chinese Large Language Model Evolution. The emergence of Large Language Models (LLMs) dominated by ChatGPT and GPT-4 [5] has revolutionized the paradigm for novel numan-machin interaction. Driven by learning-oriente, technologies [1 [11-13, 53-55, 48], pragmatic instruction [32, 47] and preference optimization [7, 34) strategies enable L.MSs to address complex generation tasks with aligned human intentions. Despite improvements, large-scale resources for training general LLMs [28, 43, 44] are anchored in the English corpora, limiting their abilities to respond reliably in extensive Chinese application scenarios. Recently, researchers [18, 59] have attempted to enhance the comprehension and execution of Chinese instructions in open-source LLMs by augmenting Chinese demands,
vocabulary and data (c.g., Chinese LLaMA and Alpaca [18]). To facilitate Chinese-specie several LLMs trained from scratch exhibit remarkable Chinese proficiency due to multilingual data resources, such as the Baichuan [2, 52], General Language Model (GLM) [20, 57], and Qwen [6] families. In this work, the Baichuan2-B BBse series is utilized as the foundation model for our PediatricsGPT, given its comprehensive potential among similar contenders.
L.MS in Medical Applications. Current L.LMs provide unprecedented opportunities to develop resource-efficient and dingmostic-oomppehensiiee intelligent healtheare systems. Despite universal

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Table 1: Statistical information on the proposed dataset. PedCorpus is well extensible and adaptable by incorporating general domain data and as seed instructions to generate specialized corpora ei.e. PedCorpus-CPT and PedCorpus-DFi DFPOO. "KG" means the Knowledge Graphs.
Dataset
Data Sources
Depurtment Number/Size
Human Preference MedKQ&A EviDiag TreRecom
Task Type
Pediatric Textbooks
Pediatries
37,284
Podiurric Guidelines
Pediatries
63.129
PedCorpus
Pediatric KG
Pediatrics
46,321
Real Doctor-Patient Conversations
Multiple
46,385
Distilled Medical Datasets
Multiple
107,177
Plain Texthooks, Guidelines. KG
Multiple
PedCorpus- CPT
CPP
Hiltered Chinese Wikipedia
Multiple
975.8MB
Extended data from PodCorpus
Multiple
PodCorpu-DFFO Podiatries data from PodCorpus
Podiatries
15,556
models [5, 33] equipped with certain internal knowledge regarding biomedicine, they are incompetent in real-world medical applications due to the absence of domain-specific disciplines. In this context, several efforts [45, 50, 15, 30] attempt to construct medically tailored LLMs from multiple perspec- tives. For instance, ChatDoctor [30] uses patient-doctor conversation data based on LLaMA [43] enhance the language model's accuracy in healthcare. DoctorGLM [50] proves that a healthcare- purpose LLM can be implemented with affordable overhead by fine-tuning ChatGLM-61 [20]. After that, more Chinese medical LLMs [51, 8, 58, 14, 56] are progressively presented to generate doctor-lik like robust responses, such as HuatuoGPT [58], DISC-MedLLM [8], and Zhongjing [56]. Despite advances in general medical knowledge, current models are suboptimal for pressing pediatric applications. In comparison, our sophisticated training procedure and high-quality instruction datasets inject new insights and prospects for developing specialized LLMs with pediatric expertise.
Methodology
This section describes the proposed PedCorpus dataset and the sequential pipeline for developing PediatricsGPT. Figure illustrates the comprehensive method workflow.
3.1 PedCorpus: Multi-task Medical Instruction Dataset
To endow the model with versatile diagnostic proficiency, PedCorpus is constructed through the multi-dimensional corpus across three application-oriente medical tasks, including Knowledge Question-Answer (MedKQ&A). Evidence-based Diagnosis (EviDiag), and Treatment Recommenda- tion (TreRecom). Table shows the detailed statistical information from different data sources. We explain the three patterns of PedCorpus construction below.
Specialized Pediatric Data. Extracting pediatric data from textbooks, guidelines, and knowledge graphs ensures knowledge professionalism. Specifically, we automatically extract standard medical definitions and descriptions from physical textbooks covering 131 disease types in 11 broad categories. Over 500 corresponding discase guidelines are collected, including diagnostic protocols and treatment consensus. Additionally, extensive knowledge entities are sampled from ternary instances in the knowledge graphs. Based these resources, introduce role-play pole-playing-dee driven instruction building rule via GPT-4 API that produces well-organized instructions to enable accurate and humanistic model responses. The detailed building procedure is shown in Appendix A.
Real Doctor-patiens Conversations. To avoid the model collapse dilemma [42], we incorporate authentic doctor-patient dialogues from online treatment platforms and voice transcriptions during medical consultations. The single-/multi iurtii-tunnii instructions are jointly considered to equip the model with healthcare interrogation and contextual understanding. Original responses from real doctors usually terse and noisy, potentially worsening the generation quality [58]. To this end, we craft 100 high-quality examples to guide the advanced language model by the in-context learning to regularize vanilla conversations in the self-instruct pattern [17, 46). This approach ensures doctor-like and patient-friendly model responses. More regularization details are shown in Appendix A.2.
Distilled Medical Datasets. Integrating general medical knowledge from existing datasets [29, 26, 60] is common practice in previous efforts [15, 50, 51, 8). However, we find numerous unclear and incomplete representations the instruction instances from public benchmarks due to the absence of careful calibration, potentially triggering hallucinated outputs. Consequently, manually sample
3

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1
Text Comphiien
Pedlaric
Paliapus Dussee
Medical Dans
Polatic Baw
AndayiosPP
Medial Genecs
Numan Frefernce
Pedistc Experiae
Medical Generala!
Hybelt Indrrction Pradiaistig Machanion
Doner Fodnacin Prefensce Optinsiaaion
Mollotapeeent Satro
Medical Oata
W.llila Alordies
Comvenations
Inenaciinn
Dalnne
Poterred Reapstors
Low Regoorse
Transkereer Beck in LLM
Devera
Krowledge
Grapta
Inatracdin Data
Padasicc Base Benr
Podetrc Bese
Datusi
Pesfimi Medical
Bass Model
Medical Doss
Oislgus Abalty
Sup beteake
Profersnos-a
Modil
Heuson Prefecene
Continuous Pre-Training (CPT)
Full-parameter Supervised Fine-luning (SFT)
Human Preference Alignment
LoRA-based Paramoter- -efentttt-efiiintt SFT
The sequential pipeline for developing PediatricsGPT. We begin by injecting intensive medical and world knowledge into the foundation model through the hybrid instruction mechanism in CPT phase. Then, -arrameeret SFT is implemented to improve the model's s instruction insllwntion-following capabilities regarding medical generalists. After that, we introduce the direct following preference optimization to control the model behaviour to align with human preference. In the parameter-efficiet SFT phase, the L-RA-ba mixture of universal-specifee experts is devised to mitigate conflicts across downstream tasks and competition between pediatric expertise and general mastery.
Figure 1:
107,177 knowledge-intensinn instructions from three mainstream benchmarks (i.e., Huatuo-266 [29], MedDialog [26], and CMeKG [10]), adhering to the philosophy of quality over quantity [62]. After that, a progressive instruction reconstruction rule is proposed to distill the sampled instructions to ensure informative and logical model responses. The rule process can be found Appendix A.3.
3.2 Hybrid Instruction Pre-training in CPT
Continuous Pre- Training (CPT) is essential in developing domain-specific models [14, 49, 56] since it can break the scaling law [24] to a certain extent. For this purpose, we introduce the PedCorpus- CPP dataset ensure high-quality pre-training corpus. From Table 1, PedCorpus-CPT consists of three- part data components. (i) We integrate plain texts from vanilla pediatric textbooks, guidelines, and knowledge graphs. (ii) The filtered Chinese Wikipedia [3] is also considered to achieve the model's trade-off for medical-genera knowledge memory capacity. (iii) In practice, we observe that CPT leads to catastrophic forgetting of the models at follow-up due to different data distribution and format discrepancies compared to the original pre-training and SFT. Thus, we introduce a hybrid instruction pre-training mechanism bridge these discrepancies. The core philosophy is assemble instruction data from PedCorpus with Input-Output forms into Completion forms, which are then assimilated into plain texts to provide multi-task and complementary information. This mechanism effectively mitigates inconsistencies between the internal-inject while reinforcing medical domain adaptation. Moreover, we take PedCorpus as the seed instructions corpus density and breadth via seowledge-enhaneee prompts. The
medical knowledge of the founddatoo model
to improve prompt template shown in Appendix B.
We pre-train the foundation model to follow the causal language modelling paradigm. Given any input token sequence t. = (lo, 4, ...) Dept from the above multi-channel corpus Dept. the next token 4 is autoregressively predicted by minimizing the negative log-likelihood:
Ccrr(0, Dept) = TenDDape Dope
[- --
log to, fi...,, 1s-1:0) :1]
where 0 is the model parameter and the input context consists of to, ti, 1/-1-
3.3 Full-parameter Supervised Fine-tuning
During this phase, we activate the model's ability to follow medical instructions by the Full- parammerr Supervised Fine-tuning (FSFT). The full-parameter pattern enables a fuller invocation of the intensive knowledge in CPT and promotes comprehension and logical reasoning about diverse structured instructions. The training data at this phase is composed of the following three aspects. (i) We utilize the multi-department medical data in the PedCorpus dataset to develop the medical generalist. (ii)

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Chinese instruction data (i.e.. Alpaca dataset [35] and ShareGPT [4]) from general domains are selectively integrated avoid the potential overfitting risk. (iii) Providing safety measures vital for I.M assistants yet overlooked by prior methods [62]. In contrast, we write 200 training instructions with some degree of maliciousness, hallucinations, and counterfactuals. Correspondingly, the refusal responses with detailed explanations for disobedience are carefully crafted. We also include 300 examples related to self-cognition content. These data significantly improve the robustness and security of the model against unfriendly commands.
Given any input instruction x (Ty, T1, I2....) E Draft and corresponding target response = you y/, v2....) E Dofft from the above soveeintegrated fine-tuning dataset D ssft, the optimiza- tion objective can formulated as follows:
(0, B(a.)))D logp(u Ne. 0
3.4 Direct Following Preference Optimization
Aligning human intention preferences facilitates the model to generate harmless responses. To this end, we introduce PedCorpus-DF-PO D pa a preference dataset to guide the model in learning human pref- erence behaviours. PedCorpus-DFPO contains the input instruction set (ro, 21, I2,,..) pa which is selectively sampled from vanilla PedCorpus. On the one hand, we perform a humanistic stylistic rephrasing of the outputs to generate preferred responses ydd (yū" 2/" 3/2 ,...) pas On the other hand, the corresponding low responses y (v6- v v..... E Dappo generated from feedback of a low-capability medical assistant [45] to maintain domain consistency.
Despite impressive improvements achieved by RLHF- based approaches [56, 58], challenges remain due unstable reward modelling and significant computational costs [41. 61]. Inspired by single- stage preference learning [38], we propose a stable and lightweight method for domain-speciffc LLMs called Direct Following Preference Optimization (DFPO). DFPO utilizes variable changes to formulate the preference loss policy function that efficiently optimizes the policy with simple binary cross-entropy objective. Meanwhile, our method directly regularizes model behaviour boundaries in an instruction-followin paradigm medical demonstrations of preferred responses, facilitating robustness and smoothing of the preference learning.
Theoretically, the observed probability of particular preference pair usually follows the Bradley- Terry model [9], and y" is preferred over y' (denoted y y'):
ply y = (7((0, y²) (2x, (x. yy)),
where (xx // means the parameterized reward function and of.)) is the sigmoid activation. In this case, the overall optimization objective is expressed as:
LDFPO(0, Dafpo) - -( "yy"""""))))) Daa log ( log
T T (y
ac
log
To ( n. (y)
+ (x, y "), x))
x
(4) where To and n. the desired optimal policy and the reference policy, respectively. B is the control parameter reflecting the deviation from the basic ,, For the fine-tuning regularization term (xx ") with the scaling coefficient μ. the implementation process is equivalent maximizing the log probability ply" x) regarding the preferred responses y" given the input instructions æ:
(xx [x, )) ( )
E 10" log P(V." |x, VZ.
(5)
3.5 Mixture of Universal-speciffe Experts in Parameter-efticient SFT
This phase aims to reinforce the model performance for various pediatric applications through the LoRA-based Parameter-efficient SFT (PSFT). The used dataset Dpsft is derived from the pediatric department in PedCorpus and partial general medical/world data. In practice, we observe that competition across different pediatric tasks and the conflicts between medical generalization and specialized knowledge deteriorate eestruction-followiin of -pieefi experts strategy to address these challenges. Formally, LoRA adapters [27] act as experts to replace the linear layers in the fred-Forward Neural (FFN) networks of LLMs, providing
abilities. Accordingly, we propose a mixture
5

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Table 2: Comparison results of different models on three pediatric medical benchmarks. In each benchmark, the best results are marked in bold, and the second-bes results are marked underlined.
Benchmark
Model
ROUGE-1
ROUGE-2
ROUGE L. BLEU-1
BLEU-2 2 BLEU-3 3
BLEU-4
GLEU
Distinct-1
Distinct 2
Baichuan2-7B
40.88
19.44
21.50
26.77
20,00
17.30
14.86
24.88
20.14
39.95
Raichuan2: 13B
46.96
22.85
22.54
29.02
25.62
22.63
19.31
27.97
21.45
42.53
HuatuoGPT
48.52
23.44
25. 13
43.00
41.25
36.31
29.82
34,60
20.42
41.27
DISC-MsdLLLM
53.83
25.98
27.71
47.91
44.57
37.65
26.63
51.98
MedKQ&A
Zhongjing
53.97
26.03
29.56
51.11
45.04
39.13
33.59
42.61
26.75
52.66
HaatuoGPT-i1
55.27
Meditron-7B
55.63
26.19
30.37
58.43
53.45
56.07
38,77
42.23
22.34
45.17
Llama3. 1-BB
53.18
24.74
28.26
45.07
42.45
36.57
29.73
35.63
22.74
46.52
ChatGPT 56.92
27.87
29.05
61.58
54.37
47.97
40.77
45.15
20.76
40.19
GPT-4
58.79
33.56
32.15
62.53
59.14
55.26
52.39
53.72
21.79
43.26
PediatrieSPPT-78
58.08
31.78
31,11
59.41
56.88
57.47
55.34
54.41
24.33
47.41
PediatricsGPT-133
60.85
36.56
35.64
61.65
63.17
58.96
59.34
57.22
24.24
46.23
Baichuun2-7B
7.75
11.22
11.51
9.19
Baichuan2-133
39.14
12.06
12.44
47.65
36,02
28.82
21.19
28.28
25.45
50.43
HuatuoGPT
35.12
10.77
15.04
46.22
33.10
25.44
21.22
25.44
22.30
45.73
DISC. MedLLMM
33.55
11.67
15.32
15.99
12.46
10.27
7.96
16.77
35.89
69.36
EviDiag
Zhongjigg
40.92
14.26
17.41
48.64 64
37.52
30.17
22.44
27.03
33.40
65.89
HuatuoGPT-II
39.52
12.14
16.38
37.62
30.66
21.97
43.62
Meditron-7B
42.63
15.12
18.94
$2.36
39.24
37.78
27.15
31.25
22.07
45.13
Llama3.1
37.25
ChatGPT 40.88
13.42
16.97
48. 84
37.69
30.55
23.17
29.02
23.49
46.54
GPT-4
48.48
16.74
21.51
57.59
78
37.94
30,56
36.79 79
25.69
50.13
Pedistries@PT-7B
45.83
16.60
19.91
54.37
41.99
37.59
29.03
33.42
23.49
46.61
PediatricsGPT-13B
47.32
17.63
21.87
58.21
45.72
39.74
31,25
37.15
23.34
46.34
Baichuan2-7B
48.39
23.07
26.35
35.54
35.06
43.57
Baichuan2 13B
48.87
23.41
26.42
49.96
46.24
42.84
35.04
35.63
22.36
45.12
HuatuoGPP
53.48
42.93
23.26
DISC-MedLLM.
52.77
24.26
28.89
58.73
50.05
42.96
35.59
42.44
24.30
51.95
TreRecom
Zhongjing
54,92
26.63
29.68
60.12
53.31
44.25
38.76
40.38
26.18
53,94
HuatuoGPTTII
58.44
30.47
32.02
59.91
54,26
45.73
38.92
42.28
28.88
57.15
Meditron-7B
58.56
32.25
33.37
60.47
55.36
48.73
42.18
46.73
28.51
57.45
Llama3. 1-8B
52.45
24.98
26.14
57.56
40.67
22.73
45.13
ChatGPT 59.59
33.34
35.79
62.81
55.79
49.85
43.29
47.59
31.09
56.87
GPT-4
36.73
PediatriesGPPT-78
56.92
29.13
31,26
61.36
55.34
46.44
40.61
44.65
26.06
52.77
PediatricsGPT-1J33
62.83
39,32
40.82
63.56
56.68
50.80
44.31
54.65
31.94
57.56
trainable parameters. Several specific experts
assigned adaptive activations master distinct pediatric expertise through soft routing. The routing gating is defined as follows:
{ (F)) )
G(x) = So + S((p(x).)).
(6)
W, and W. are the learnable weights. S(p(=W,,) the noise term for regularizing the expert utilization balance, where S(.) and sp-)) represent the Standard Normal distribution sampling and Softplus function, respectively. Moreover, we consistently activate a universal expert E" across all training data to prevent general knowledge forgetting and mitigate competency conflict. The parameterized output of all the experts in the forward process can be mathematized as follows:
T
a
(o)) + E (x)),,
z
==1
where is the rank value and is capper-parameter for approximating the learning rate.
4
Experiments
4.1 Datasets and Implementation Details
Extensive experiments conducted on three application-oriented benchmarks to assess the model's pediatric medical abilities, including Knowledge AresstionAAswer (MedK&&A) Evidence based Diagnosis (EviDiag), and Treatment Recommendation (TreRecom). Each benchmark contains 300 held-out samples to reject data leakage during training. In addition, we select two publicly available Chinese medical benchmarks to validate the model's generalizability in general healthcare. Specifically, we sample 50 challenging instances of diagnostic queries from each department from the MebMedQA [25] and CMD [1] benchmarks, respectively, leading to testing sets with 300 samples.
Our PediatricsGPT developed upon the Baichuan2- [52] models in two versions with 7 and 13 billion parameters. The model training is accomplished through the Py Torch platform with Accelerate

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Wis
Bairhman2-11B
Baichmnnd-
ButuuGP
HustoGa
16
15
HustsGPT
DISC-MoiLLM
DISC.M
DISCMMiLLMM
Shrgging
Zhinging
Zhanging
Buttorr--I-
MuaraatiPT-I--
CharGPT
ClaCPT
ChatPT
(86))
1974
4185
198%
(a) MadKQRA Benchmark Evaluation
(b) EviDing Benehmark Evaluation
(c) TreRecom Renchmark Evaluation
Figure Response comparisons of PediatricsGPT-13B with other baselines via GPT-4 evaluation.
Oven Wix
Ours
OOen
Doa
Oven Wax
Our
Rbichunn2-131
72
Raichran2-130 1311-
BuatuCPT
HustwoPT
Hasts:PPT
DISC-MeILLM
MISC-MeeLLA-
DISU-MelLLMM
Zhogging
Zhongling
Zhinging
71
HustunGPP--
20
10
HustoGPPT-1
11
ChaGGPT
ClasGPT
ChatPT
24
(a) MedKQ&A Benchmark Evaluation
(b) EviDing Benehmark Evaluation
(c) TreRecom Benchmark Evaluation
Figure 3: Response comparisons of PediatricsGPT-13B with other baselines via Doctor evaluation.
and DeepSpeed packages using eight Nvidia A800 GPUs. The ZeRO strategy [39] is employed to alleviate the memory overhead during full parameter training. The AdamW optimizer [31] adopted for network optimization, and the 6 data accuracy is chosen. More detailed configurations for different stages are shown in Appendix C.
4.2
Model Zoo
We compare series of LLMSs for comprehensive evaluations. Concretely, Baichuan2-7B/13B (Chat) models [52] are trained 2.6 trillion tokens as the baselines, which have excellent abilities in different domains. Meditron-7B [16] is a billion parameters model adapted to the medical domain from Llama2-7B through continued pre-training on a comprehensively curated medical corpus. Llama3.1- 8B [21] is robust multilingual large language model through systematic training. For reproducible Chinese medical works, DISC-MedLLM (13B) [8] fine-tuned through reconstructed medical dialogues and behavioural preference instructions. HuatuoGPT (13B) [58] performs SFT based on mixed instruction data and introduce human feedback in RLHF. HuatuoGPT-II (13B) [14] enhances the medical-specine domain adaptation of LLMs through one-stage unified training. Zhongjing (13B) [56] implements a complete pipeline based on Tiya-LLaMA-11BB to enhance the model's multi- turn medical conversation abilities. ChatGPT [33] and GPT-4 [5] have impressive performance in general medical ficlds as closed-source models developed by OpenAl.
4.3 Comparison with State-of-the-art Methods
Metrics-based Evaluation. In Table 2, we present the comparison results of different models on three pediatric benchmarks through multifaceted metrics, including ROUGE- 1/2/L, BLEU-1/2/3/4, GLEU, and Distinct-172 1/2. The key observations are listed below. (i) PediatricsGPT-133 significantly outperforms the baselines and SOTA medical models on the vast majority of metrics across all benchmarks, demonstrating excellent pediatric expertise. (ii) Our 7B version also achieves competitive results compared to the 13B models. For instance, PediatricsGPT-7B yields absolute improvements of 3.53% and 4.44% metrics ROUGE-L and GLEU in the EviDiag task compared to HuatuoGPT-II. respectively, generating more accurate and informative content. (iii) By contrast to Zhongjing and HuatuoGPT-II with massive training corpora, our method confirms that the training data quality outweighs quantity for performance gains. (iv) The worst results at baselines emphasize that target- oriented fine-tuning is an effective strategy for improving
abilities.
Automated GPT-4 Evaluation. Measuring model performance from multiple aspects is essential in the pediatric medical domain. To this end, we consider four dimensions to holistically assess response quality, including usefulness, correctness, consistency, and smoothness. Advanced GPT-4 [5] prompted to select the winning response between pairwise models based on these dimensions. The

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GLBU Score (%)
25-
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Gynerology
Interual Medicine
Audrology
Oucology
Podiaccics
Surgery
Medical Departments
Figure 4: Comparison results of different models on the CMD benchmark.
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Figure 5: Comparison results of different models on the webMedQA benchmark.
dimension explanations and the prompt template for GPT-4 can be found in Appendix D. (i) As Figure 2 shows, PediatricsGPT-133 wins all LLMs by large margins in the MedKQ& A task, implying the necessity of implementing the mnowledge-intensive CPT. (ii) The favourable win rates on the TreRecom and EviDiag tasks compared to medical LLMs show the superiority of our model in both single-turn treatment recommendations and multi-turn medical diagnostics. For example, our model beats Zhongjing via the 59% win rate on the EviDiag, which specializes in multi-round consultations.
Manual Doctor Evaluation. Doctor approval of LLM assistants is a vital step toward realistic applications. We invite three doctors (each paid $300) to determine the winner of pairwise models by the majority voting rule. The evaluation requires simultaneous consideration of the responses' prefessionalism, factuality, and safety.
(i) Excluding ChatGPT, the dominance of our model in Figure shows the effectiveness of considering safety measure data while incorporating specialized pediatric knowledge. (ii) The proposed direct following preference optimization makes PediatricsGPT- 13B more favoured by human preferences compared to other behavioural alignment efforts [8, 56, 58). (iii) The competitive performance of ChatGPT when human judgments indicate that the scaling law still holds, stemming from the high agreement between its behaviours and human intentions.
Generalization Ability Evaluation. We show the GLEU metric-based scores of different mod- els on the Chinese medical benchmarks in Figure 4 for CMD and Figure for webMedQA. (i) PediatricsGPT-133 achieves impressive results across diverse medical departments (including pe- diatrics), exhibiting medical generalist and pediatric competency mastery. (ii) The 7B counterpart similarly outperforms most 13B Chinese medical LLMs and exceeds ChatGPT in some departments. For instance, PediatriesGPT-7B brings relative gains of 18.8% and 7.1% compared to ChatGPT in the Gynecology and Oncology tasks on the CMD benchmark. These findings confirm the robust generalization of our model and its ability to capture the multifaccted medical dialogue distributions.
4.4 Ablation Studies
We perform thorough ablation studies on five medical benchmarks to investigate the effects of different modelling components. Following [58]. we compare the responses from each of the proposed model variants with ChatGPT, and then calculate the win rate (%) of our model in pairwise responses by GPT-4 and doctor evaluations. Table 3 shows the following observations.
Importance of Continuous Pre-training. Firstly, we remove the complete continuous pre-training phase to observe performance variations. (i) The significantly deteriorated win rates reveal that
8

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Table 3: Ablation study results on five medical benchmarks. "w/" and "w/o" are short for with and without, respectively. "MUE" means the Mixture of Universal-speciff Experts strategy.
MedKQ&A
EviDiag
TreRecom
CMD
webMedQA
Components
GPT- Doctor GPT-4 Doctor GPT-4 Doctor GPT-4 Doctor GPT-4 Doctor
Full Model
68%
56%
54%
44%
58%
38%
46%
40%
53%
45%
Importance Continuous Pre-training
w/o Continuous Pre- Pre-training w/o Hybrid Instruction Pre-training
61%
50%
46%
39%
51%
31%
39%
33%
47%
38%
67%
54%
52%
43%
57%
36%
44%
38%
52%
43%
Necessity of Supervised Fine-tuning
w/o Full- parameter SFT
65%
53%
50%
42%
55%
36%
42%
36%
49%
41%
w/o Parameter-efficient SFT
63%
51%
49%
40%
53%
34%
45%
39%
51%
43%
w/o MUE Strategy
67%
57%
52%
43%
56%
36%
43%
37%
50%
42%
w/o Universal Expert
67%
56%
53%
44%
57%
37%
44%
38%
51%
42%
Effectiveness of Preference Alignment
w/o DFPO w/ Vanilla DPO
67%
53%
52%
41%
57%
36%
45%
38%
51%
41%
66%
55%
53%
42%
57%
36%
44%
52%
42%
RLHF
67%
55%
54%
43%
57%
37%
45%
39%
52%
44%
injecting specialized knowledge into medical LLMs through rich corpora is indispensable. iii) Meanwhile, our hybrid instruction pre-training mechanism provides valuable gains to the model.
Necessity of Supervised Fine-tuning, (i) We observe consistent perfor- mance gaps when removing the Full- parameter SFT (FSFT) and Parameter- efficient SFT (PSFT) phases, respec- tively. This makes sense since SFTs necessary to activate the model's healthcare citructtio-followinng capa- bilities. (ii) Moreover, PSFT is more critical for three pediatric applications because it facilitates pediatric-relatee knowledge accumulation, while FSFT focuses consolidating general med- ical semantic representations. (iii) Then, we replace the proposed Mix- ture of Universal-specifi Experts (MUE) version with the vanilla single LoRA. The reduced per- formance on pediatric EviDiag and TreRecom benchmarks verifies that it essential to introduce multiple LoRAs that act as specific experts on different tasks. A reasonable explanation is that the single-LoRA model suffers from the task competition between learning the knowledge question- answer and mastering the diagnostic recommendation abilities. (iv) Furthermore, we find that the universal LoRA expert significantly improves the results on the general medical benchmarks (i.e., CMD and webMedQA), proving that it mitigates the competency conflict between general medical and pediatric knowledge.
Select speciti
Wie Rlaee oil
MoIkQEA- 00.55
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0.74
1.11 0.15
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51
53
TeeRenam Yeak
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0.24
0.5
0.1
0%
10%
60%
50%
100%
Speettfic Eispert Numbberx (P)
Speciiii
Utiliantion
(a)
Figure 6: (a) and (b) show the effect of specific expert num- bers on model performance and specific expert utilization in different task data, respectively.
Effectiveness of Preference Alignment. (i) When the Direct Following Preference Optimization (DFPO) phase is removed, the model exhibits significant performance drops in doctor evaluations compared to the full version. This observation proves that DFPO effectively helps the model to align human preferences, reducing harmful content while generating doctor-like output. (ii) As two candidates, the vanilla DPO and RLHF methods are inferior to the proposed DFPO, suggesting that our strategy can more safely control model behaviour, leading more favoured humanistic responses.
4.5 Qualitative Analysis of LoRA Experts
Effect of Specific Expert Numbers. As a complement to the ablation of LoRA experts, Figure 6(a) explores the gain effects of varying the number of specific experts while maintaining the universal expert. (i) Noticeably, our MCE strategy with three specific experts achieves a reasonable performance trade-off across the three tasks with only 0.95% trainable parameters. (ii) Conversely, excessively introducing LoRA experts does not result in appreciable gains but increases the training overhead.

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Analysis of Expert Utilization. To confirm the duties of specific LoRA experts in the routing process, we visualize the normalized weights assigned by the routing gating when encountering data from different downstream tasks. CMD and webMedQA data are merged to compose general healthcare data. From Figure 6(b). (i) Experts and are emphatically activated on the TreRecom and MedKQ&A tasks, respectively, implying their focal ability to tackle medical knowledge inter- pretations and treatment recommendations. (ii) In contrast, Expert I is more proficient at learning multi-turn diagnosis semantics in the EviDiag task, which is different from the other tasks of instruc- tion content. (iii) Additionally, there is no clear difference in the specific expert utilization on general healthcare, implying that the general task is handled by the consistently universal expert. The above observations demonstrate the effectiveness and necessity of the proposed MCE strategy.
4.6 Visualization Analysis of Model Responses
To intuitively compare the output quality of medical LLMs, show the responses of different models for each of the three types of medical inquiries in Figures 13& 14& 15 from Appendix E. From the results, Zhongjing offers insufficient information due to limited output content. Although HuatuoGPT-II gives well-organized responses, it lacks accuracy and informativeness. In comparison, our model provide more specialized and detailed medical knowledge and diagnostic guidance in extended response contexts, confirming its application potential in diverse healthcare services.
5 Conclusion and Discussion
This paper presents PediatricsGPT. a Chinese medical LLM assistant with medical generalist and pedi- atric expertise capabilities. Based on the well-designed PedCorpus dataset, PediatricsGPT undergoes a systematic and robust procedure ranging from continuous pre-training and supervised fine-tuning to human preference optimization, leading to competence in different pediatric and general healthcare service scenarios. Extensive experimental results under strate that our model outperforms currently available Chinese medical LLMs, providing potential solution for promoting reliable and intelligent interactive diagnosis and treatment.
evaluation patterns demon-
Broader Impacts. (i) Our model has made meaningful contributions to pediatric medicine by integrating extensive medical data and emerging research. This integration facilitates more accurate and expedited diagnosis of complex pediatric conditions and aids in predicting treatment outcomes, enabling highly personalized and effective treatment strategies for young patients. (ii) The proposed PediatricsGPT provides crucial decision support for medical professionals, giving evidence- based recommendations and specialized medical insights. Additionally, it democratizes access to expert medical suggestions and accurate medical knowledge, empowering parents and caregivers with accurate health information, which is especially crucial in underserved areas. (iii) The training pipeline of PediatricsGPT showcases exemplary generalizability, designed to be applicable across various medical and non-medical domains. This adaptability broadens the model's applicability and pioneers the development of future AI solutions in healthcare and other fields.
Limitations. (i) When deployed online, the proposed PediatricsGPT model, like other Large Lan- guage Models (LLMs), faces significant security risks, particularly from attacks aimed at manipulating its outputs. These attacks can strategically designed to exploit the model's response mechanisms, allowing attackers to induce the model to generate unsafe, biased, or otherwise inappropriate content. (ii) Currently, our PediatricsGPT model does not support all languages. This linguistic barrier can prevent the model from reaching global audience, particularly in diverse linguistic landscapes where localized medical information is crucial.
Ethical Issues. We fully recognize the critical importance of privacy and data protection. All data used has been meticulously de-identified, with all sensitive information removed, and this process has been verified by the partnering medical institutions. For the public databases, we strictly follow specific license agreements for use and adaptation. For the constructed corpus, we underwent an internal ethical review by the ethical review board of the partnering medical institutions with license and approval. We will release relevant resources the extent that they are controlled and permitted.
We provide more discussions of the future work in Appendix F.
Acknowledgment. This work is supported in part by the National Key R&D Program of China under Grant 2021 13502, in part by the Shanghai Municipal Science and Technology Major Project under Grant SHZDZXX100..
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14

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A Implementation Details of PedCorpus Construction
A.1 Role-playing-driven Instruction Building Rule
After integrating pediatric textbooks, guidelines, and knowledge graphs into a consolidated textual database, the content is segmented according individual discases. Subsequently, two instances of the GPT-4 model are deployed, designated as the "inquirer" and the "expert pediatrician" respectively. Discase-specifie segments are then fed into the "inquirer" GPT-4, tasked with formulating series of relevant and scholarly pediatric inquiries. Following this, the original disease segments and the formulated inquiries are fed into the "expert pediatrician" GPT-4 to generate precise responses for each inquiry, leveraging the segmented text as the contextual reference. We show the prompt templates for the "inquirer" and "expert pediatrician" in Figures and 8, respectively.
A.2 Vanilla Doctor-patient Conversation Regularization
We guide GPT-4 regularize concise and noisy doctor responses in authentic doctor-patient con- sultations by the context learning strategy. Specifically, we manually craft 100 instruction examples with high-quality content to allow GPT-4 to learn doctor-like and patient-friendly behavioral styles. In each round of regularization, we randomly sample 10 out of 100 examples to perform 10-shot context prompts. Immediately after that, vanilla dialogues are fed to GPT-4 as seed instructions to optimize instructions according to user requirements. Constrained by the space, we show the prompt case with one example Figure 9.
A.3 Progressive Instruction Reconstruction Rule
Medical knowledge integration from existing datasets is common but frequently imprecise, resulting in unclear or incomplete instructions and potentially inaccurate outputs. Consequently. 107, 177 instructions are selected from three significant benchmarks, prioritizing quality over quantity. this case, we design progressive instruction reconstruction rule to refine these instructions, ensuring informative and logical model responses.
As shown in Figure 10, we first prompt GPT-4 to take the perspective of the experienced doctor to complete Tasks and in the given instruction and answer scenarios. Task 1 focuses on bridging the gaps in the vanilla instructions and reinforcing the completeness, professionalism, and medical relevance. Based on the refined instructions, Task requires the GPT-4 to make further targeted improvements to the answer parts. In practice, this progressive reconstruction rule can activate better instruction following capabilities in advanced language models.
B Knowledge-enhaned Prompt
To enrich the density and breadth of the instruction data from the vanilla PedCorpus dataset into comprehensive medical knowledge texts using knowledge-enhanced prompts. The medical knowledge texts are integrated as complementary content to construct the PedCorpus-CPT dataset. The prompt template is shown in Figure 11.
CPT corpus, we transform the structured
C Training Details
In this section, we list in detail the hyper-parameter configurations for the different training phases.
Continuous Pre-training. During this procedure, we train each model for just a single epoch, setting the learning rate at lc-6 and the batch size at 128. We adopt maximum cutoff length of 4096, enabling the model to process extensive text sequences in one batch. This significantly enhances the model's 's contextual understanding and coherence.
Full-parameter Supervised Fine-tuning. In this configuration, we train models for three epochs with learning rate adjusted to 5e-5 and a batch size of 64, capping the maximum sequence length at 2048. We introduce warmup_sted setting at 200 to gradually ramp up the learning rate from an initial lower value, aiding the optimizer in adapting to gradient changes. This approach boosts stability and performance and guides the model towards better convergence path. Also, we specify eval steps at 100 and save the best-performing weights the validation set to ensure optimal results.
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Human Preference Alignment. In this setup, we train five epochs with the learning rate set to 1e-6 and the batch size maintained 64. To enhance the robustness and smoothness preference learning, we adjust the control parameter 3 to 0.1 and the scaling coefficient to 1.0. We specify eval_steps at 100, selecting the best-performing weights on the validation set.
LoRA-based Parameter-eflicien SFT. Here, we train three epochs with learning rate of lc-6 and adjust the batch size to 32. We configure the LoRA parameters by setting the rank r to 8, 16, and the Dropout rate to 0.05, targeting all modules. The default number of LoRA adapters is set to 4, including one constant universal expert and three specific experts. Ultimately, we select the adapters that perform best on the validation set.
D GPT-4 Evaluation Details
We consider four complementary dimensions in the automated evaluation to guide GPT-4 judging the quality of model responses from a comprehensive perspective. The full definitions of these dimensions are shown as follows.
Usefulness;
measures the extent to which the model response has pediatric expertise and relevance to
the instruction intention.
Correctness:
measures the extent to which harmful, misleading, and inaccurate information is present in the model response.
Consistency: measures the degree to which the model response logically self-contradictory and the information is coherent in context.
Smoothness: measures whether the response content is fluent, natural, and conforms to the language expression style of human habits.
In this case, we present GPT-4 with paired responses from different models, assessing various criteria such as pediatric expertise in the responses, presence harmful, misleading, or inaccurate information, logical consistency, and the fluency and naturalness of the language, which should conform to human linguistic habits. GPT-4 assesses these responses on their merits and selects the superior one. To maintain fairness and mitigate potential position bias, the order of the responses is randomised. This methodology is supported by recent studies demonstrating GPT-4'ss strong agreement with human judgment in evaluating responses. Figure 12 demonstrates the prompt template used to evaluate the quality of paired model responses.
E Comparison Results of Model Responses
this section, we visualize the responses of the proposed PediatricsGPT-133 and two SOTA Chinese medical LLMs across three tasks from the same medical inquiries to provide intuitive qualitative comparisons. Specifically, Figures 13 and 15 illustrate the medical knowledge question-ansee answer and treatment recommendation tasks, respectively, which follow single-ture dialogue pattern. The multi-turn conversation pattern is considered in the evidence-based diagnosis task from Figure 14.
Future Work
We list future work below to provide potential optimization directions.
Enhancing Security Against Model Manipulation. To mitigate the security risks associated with online deployment, our future strategy involves implementing multi-layered security measures for the proposed PediatricsGPT model. This will include advanced input validation techniques to detect and neutralize potentially malicious inputs that could manipulate model outputs. Continuous updates and patches will also be prioritized to address emerging security threats and vulnerabilities.
Expanding Language Support. To overcome the challenge of incomplete language coverage, we are committed to broadening the linguistic capabilities PediatricsGPT.. This expansion will involve training the model on more diverse dataset that includes broader range of languages and dialects, particularly those prevalent in underserved regions. By doing so, we aim to make the model more accessible and useful to global audience, ensuring that non- on--Cheesse speakers also benefit from reliable and localized medical information.
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INST) aci as an inquirer with broud reserve of pediatric knowledge and complete the following requirements:
I. Based on the rich corpus of pediatric knowlodge, carefully formulate a scries valuuable logical and inspiring questions:
2. Ensure that the uniqueness each question designed to comprchensivelyy cover the needs of medical applications such pediatric knowledge and answers, the consultation process, and advice diagnosis and treatment of diseases, and avoid simplicity or repetitiveness questions;
3. All questions should be strictly limited to the scope of language processing, does not involve pictures, audio and other non- verbal form of the question, and shall not contain any sensitive or private information that may be involved in the real world:
4. Output the generated questions in the following format: ("Q1": "Q2". "". "Qn": "");
Please generate the questions Frrectly</SVSS//iee
[NNST
[Auxiliary Examinations for Pediatric Craniopharyngional
1. Laburalory Tests
(1) Measurement of anterior pituitary hormone levels: Cortisol (F), Adrenacorticoirpii Hormone (ACTH)) thyroid function [Free Triiodtthyonnin (FT3), Free Thyroxine (FT4), Thyraid Stimulating Hormone (TSH), etc.], Growth Hormone (GH) levels, Insulin-like
Growth Factor (IGF-I) levels, six hormones [Follicle Stimulating Hormone (FSH), Luteinizing Hormone (L.H), Testosterone (T), Estradiol (E2), Progesterone (P) and Prolactin (PRL),, and 24-hour urinary free cortisol; moming cortisol levels between 3mg/L.
18mg'L require ACTH stimulation test.
(2) Patients with significant polydipsia and polyuria: Monitoring of bloed ion levels (potassiom, sodium, chloride), plasma osmolarity, 24-hour urine volume, 24-hour urinary free cortisol, urine spocific gravity, urine osmolarity, and urinary clectrolytes. For patients with difficullto-diianees central diabetes insipidus, vasopressin test should be performed determine the presence central diabotes insiiidu..
(3) Blood tests (and cerchrospinal fluid when necessary) lumer markers such as Alphafettpprolein (AFP) Human Charionic Gonsdolrupin (IIG) for differential diagnosis.
Imaging Studies
(1) Wrist joint X-my, AP view: Mensuring hone age to understand child growth and development further.
sellar region can grow various directions with significant individual variation. Radiologically, they appear round or lobulated. The tumors may be eystic, solid, or mixed. Cystic fluid typically shows low density on CT. Solid components appear as uneven, iso-, or slightly hyperdense. Typical craniopbaryngiomas on CT may show "eggshell"
(2) Head CT: Craniopharyngionas
caleifications.
(3) Head MRI: Due to varying components, MRI signals vary. Cystic lesions mostly appear as high signal on T2W1, some as low signal
(due to keratin or calcium salts). and II signals vary depending on components, appearing low (contains dooxyhemoglobin) or high the solid parts may show varying degrees of enhancement. (4) Other imaging studies such as CTA, MRA, Digital Subtraction Angiography (DSA), etc., lo evalunte the relationship between the tumor and blood vessels.
(high protein content); post-enhuncemeen
[Differential Diaguosis of Pediatric Craniopharynglomal
1. Biocellular Tumors
Teratomas, mixed germ cell tumors etc., also show calcifications on CT. Mature leratomas have negative tunor markers like AFP, HCG, bui often contain tissues such as fat that provide mixed densities for differential diagnosis; mixed germ cell tumors have varying marker levels depending on the proportion of compoments, which can be distinguished from craniophary nigiomas.
2. Optic Pathway Glioma
Clinically may manifest as visual impaireent but typically shows as low-density lesions on CT without clear calcifications; on MRI, TI WT is iso- or hyperintense, T2WT is hyperintense, and enhancement is usually uniform after contrast adminisrration; can be distinguished from crnnoophrryngiomss htteoommasssSSSSREEE <USER=/INST]
JOUTPUTJESSSSTANN
"QI": "What are the key laboratory tests required for diagnosing childhood craniophary and how de specific hormone levels impact the evaluation!".
"Q2": "Which specific blood tests and measurements necessary for patients with pronounced symptoms of polydipsia and polyuria the context of childhood craniopharyngioma""
"Q3": "How do imaging tochniques like wrist X-my, CT, and MRI contribute to the diagnosis and cvaluation of childhood craniopharyngions?""
"Q4": "What di iifereentittes the imaging features of craniopharyngions from other similar intracranial tumorr such teratomas mixed germ cell tumors CT and MRI scans?"
"QS": "Can you discuss the significance of tumor markers such as AFP and HCG in the differential diagnosis of childhood emniopharyngioms""
"Q6": "How can the presence or ahsence of calcification help distinguish between craniopharyngioma and other types of brain tumors like optic pathway gliomas inaging studiex!"
SEASSISTANISOOUPPUTT
Figure 7: Illustration of the GPT-4 prompt template for the "inquirer" role. The original Chinese
content is translated into the corresponding English version to facilitate intuitive understanding.
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=SYS-
experienced pediatricaan and fulfill the following requirements:
1. For each inquiry, provide comprchensive responses, taking into account the intricacies of podiatric healtheare and needs of patients, cnsuring sccuracy and humanistic care in the answers.
2. Answers should be based en the provided podiatric corpus, focusing on pediatric healthcare to ensure professionalism and relevance.
3, Answers should he logically coherent and semantically clear.
4. Generated answers should be formatted ""AI":"""A""""" """" "An":""; where each "An" represents independent question.
Please generate the responses Sireell.</SVS-//NNSS

-USER>>
"QI": "What the key laboratory lests required for diagnosing childhood craniophuryngguumu, and how do specific hormone levels impact the evaluation?".
"Q2": "Which specific blood tests and measurements are necessary for patients with pronsunced symptoms of polydipsia and polyuria in the context of childhood craniopharyngioma?"
"Q3": "How do imaging techniques like wrist X-ma,, CT, and MRI contribute the diagnosis and evaluation of childhood cramioplaryngiomaa..
"Q4": "What differentiates the imaging features of cramiopharyngiona from other similar intracranial tumors such as teratomas or mixed germ cell tumors on CT and MRI scans"".
"Q5"; "Can you discuss the significance of tumor markers such AFP and HCG in the differential dingnosis of childhood craniogharyngiomaa".
"Q6": "How can the presence absence of caleiffication help distinguish between craniopharyngioma and other types of brain tumors like optic pathway gliomas on imaging studies?"
[</UEE--/INST]
(OUTPUT/KASISTTAND:
"AI": "Diagnosing childhood craniopharyngiomaa involves various key laboratory tests, notably hormenal evaluations, to assess endocrine function. Specific tests include measurements of senum cortisol, ACTH, thymid-stimulatie stimulating hormone (TSH), free T4, prolactin. and growth hormone levels. Low levels of these hermones can indicate hypothalamic pituitary dysfunction, common cramiopharyagiomaa patients due to the tumar's proximity monitoring them helps in assessing the extent the tumor's impact endocrine system.",
structures. Anterior pituitary hormone deficiencies hallmark, and
"A2"; FFor patients with pronounced symptoms of polydipsia and polyurin, specific blood tests include serum sodium, osmolarity, and blood glucose levels to rule out diabetes mellitus. Additionally, measuring serum antidiuretic hormone (ADHI) levels is crucial to differentiste between central and nephrogenic diabetes insipidus. Urine osmolality and water deprivation lest followed by desmopressin (ADH analog) administration can help confimm central diabetes insipidus, which is often associated with craniophanyaaiooma."
"A3"; "Imaging techniques such as wrist X-may, CT, and MRI are pivotal in diagnesing and evaluating childhood craniopharyngioma.. Wrist X-rays can reveal delayed bone age, indicative of growth hormane deficiency. CT scans provide detailed images of bone structures and can detect caleifications commonly seen in craniopharyngioms.. MRI is the most informative, offering high-resolstion images of soft tissues, facilitating the identification cystic components, solid tumors, and involvement adjacent structures like the hypothalamus and optic chiasm.",
"A4"; "Craniopharyngiomas often present with a mixture of cystic and solid components and frequent calcifications visible on CT scans. In contrast, teratomas may also show calcifications but often have fat and different tissue types typical of germ cell origin. Mixed germ cell tumors may present with hemorrhagic and necrotic craniopharyngiomas. MRI helps distinguish these tumors based on their unique signal characteristics and anatomical relationsbips." "A5": "Tumor markers such as alpha-fetoprotein (AFP) and beta-buman chorionic gonadotropin (HCG) are signiticant in differentiating cramiopharynginng from germ cell tumors, which often elevate these markers. Craniopharyngiomms typically do not alter AFP or IIGG levels, normal levels of these markers can help exclude genn cell tumors and support the diagnosis of craniophary "A6": "Calcification is promineat feature of craniopharyngiomas and is readily detected on CT scans. In contrast, optic pathway gliomas, typically asseciated with conditions like neurofibrematosis typo usually lack calcification and present fusiform, cahancing Thus, the presence calcification strongly suggests craniopharyngioma. over optic
and elevate tumor markers like AFP and HCG, which not elevated in
lesions along the optic nerves or chiasm pathway glioras.""
AAASSISTANT/JOUPUT
Figure 8: Illustration of the GPT-4 prompt template for the "expert pediatrician" role. The original Chinese content is translated into the corresponding English version to facilitate intuitive understand- ing.
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JNSTTJ-SSSPPP Pleaase act as an Al metical assistant and complete the multi-round medical dialogue refinement task by referring to the already refined honest medical- patient dialogue Med-D. The specific requirements are as follows:
1. Keep the patient's questions from the ori original dialogue and only refine the doctor's responses. Mimic the response style of the doctor in D, ensuring medical professionlism while making the doctor's responses both professional and patient-fricndly.
2. For questions posed by the patient involving images, audio, or other nou-verbal forms, clearly respond: "I apologisc, but as an Al assistant, I camnot receive or process any files or images. I can only communicate and provide information through text."
3. The generated dialogue should be ouiput in the following format: ("Patient".". "Doctor".".. "Patient"". "Dector".").
4. The refined medical-pali patiint dialogue Med-D is S
"Patient": "Kidney disease. There is slight swelling in the lower legs; ultrasound shows a 0.6 cm kidney stome in the left kidney, multiple kidney eysts, slight hydronephmsis, diffuse changes in the renal parenchyma, and a shrunken left kidney (8 cm)) Kidney function tests show urea at 7.26, others normal, and kidney function urine alpha-1 I microglohulin at 10.13, Urinalysis shows red blood cells at 31 45, occult blood positive, and other items normal. Taking quarter of an antihyperensive pill daily, blood pressure has been normal for the past one two years." "
"Doctor": "Your kidney issues include a left kidney stone, multiple eysts, slight hydronephrosis, changes in the renal parenchyma, and a shrunken loft kidncy. The high red blood cell count in your urine suggests minor bleading. and elevated alpha-1 microglobulin may indicate kidney damage. Here's summary:1. Stones and Cysts: The stone might obstruct urine flow, and the cysts could impact kidncy funetion, Shrinkage in your left kidney may result from these structural changes 2, Renal Changes and Hydronephmosis: These signs suggest tissue damage and reduced kidney function.3. 3. Tests: High red blood cells and positive occult blond urine tests point to slight bleeding, Your urea levels are normal, but further detailed checks necessary.4. 4, Management: Regularly monitor kidney function, maintain blood pressure with antihypertensive medication, and make dietary and lifestyle changes to reduce kidney stress, like limiting sodium and staying hy hydrated. Avoid kidney-damaging medications."
"Patient": "Thank you, what medication should take?"
"Doctor": "I cammot provide personalized medical advice, can offer general information and suggestions:1. Blood Pressure Contrel:
Continue taking your prescribed antilty pertensive medications, as managing blood pressure crucial for reducing kidney stress.2.
Prevention of Stones: Depending on their type, your docter may recommmend medications like polassium citrate or hydrochioouthiazidee
(ICTZ) to manage calcium and uric levels in your urine.3. Kidney Function Protection: Medications such as angiolensin converting enzyme inhibitoes (ACFIs) or angiotensin receptor blockers (ARBs) may recommended help protect your kidney function. Urinary Tract Health: For urinary tract infections, antibiotics may necessary. Use NSAIDs cautiously as they can worsen kidney damage.4. 4. Hydration: Adequate hydmation can help prevent stone formation and flush the urinary tract.5. Diet: Maintain low-salt, low- protein diet, limit meat and other protein-rich foods, increase fruit and vegetable intake, manage calcium intake properly, and avoid high- oxalate foods like spinach and strawberries.' "
Please follow Med-D's style and generate compliant dialogue directly.
</SYS*/INST]
UNSTI|USER>*
"Patient": have muscle sereness in my limbs and rod patches. Tripterygium wilfordii was very effective before, but can't take due stomach ulcers. symptoens recurred after I stopped taking it." "Doctor": "I'm very surry hear about your discoenfort. Based on your description muscle soremess the lower limbs accompaniee by red patches, could be related to various diseases. suggest you visit hosgital further examination. " "Putient": "The doctor saw said it's vasculitis""
"Doctor": "Understood, Vasculitis possibility, but there are many types of vasculitis, To confirm the diagnosis, recommend you visit dermatology department for further examination." |</USRR>//NST]
JOUTPUT|KASSIST
"Patient": "I have muscle soreness in my lower limbs and red patches. Tripterygium wilfordii was vary effective before, but I can't take due to stomach ulecrs. The symptoms recurred after I stopped taking it.".
"Doctor": "Since Triplerygium wilfordi isn't suitable due to your stomach uleers, you'll need alternative ways to manage your symptonss. Consulling with your healtheure provider is crucial lailer trealment plan, especially as your symptoms suggest multiple polential underlying ing conditions.
Apply warmth muscles to increase blond flow and relieve pain. Warm compresses also soathe inflamed red patches, depending their cause.2. alleviate muscle soreness. Be gentle avoid worsening the 3.in.3. Over-the- Counter Pain Relief: Consider using acstaminophen or topical NSAIDs (if approved your doctor) manage pain and inflammation. Use oral NSAIDs cautiously because of your stomach.4. Hydration and Nutrition: Keep hydrated and maintain balanced diet to support muscle and skin health.S. Skin Care: Adopt gentle skin care routines, avoid irritants, and consider hypeallergenic products, depending on your specific skin condition."
Here
medical stralegies help manage your muscle soreness and skin symploms: Warm Compresses:
Gentle Exercise and Stretching: I Light movement
"Patient": "The doctor saw said it's vasculitis."
"Doctor": " "Thanks for sharing your situation. Managing vasculitis can be tricky, especially with stomach uleers. Since Tripterygium wilfordii isn't an option, consider these altematives with your doctor:1. Corticosteroids and Immuncouppesssanss: These reduce inflammation but must be used carefully due to your ulcers.2. Stomach Protecting Medications: You might combine an intiiinllammalrry treatments with medications like peoton pump inhibitors H22 blockers to protect your stomach.3, Biologic Therapies: Targeted biologies can effectively treat vasculitis with potentially fewer stomach risks.4 Lifestyle Modifications: Diet adjustments, stress management, and avoiding smoking can help control vasculitis symptoms and prevent flare-ups.5. 5, Regular Monitoring: Since vasculitis can impact various organs, regular health checks are oricial." ANT:/UTTTT
Figure 9: Illustration of the GPT-4 prompt template for vanilla doctor-patien conversation regulariza- tion. The original Chinese content is translated into the corresponding English version to facilitate intuitive understanding.
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[LNST]<SYS> assume role experienced doctor, you now provided with a pair of medical instruction data, which includes the fields 'instruction' and 'answer'. Please complete Task and Task according to the following guidelines: Task I: Refine and expand the 'instruction' field.
Requirements:) Utilize your professional medical knowledgs to expand 'instruction', turning into medical question of depth and relevance. The question should be specific, clear, and capable of eliciting detailed medical discussions. "Instruction' should contain sufficient background information to facilitate understanding and resporse. Task 2: Based on the refined 'instruction' from Task 1, write the ouupuu' field.
Requirements:1 I. 'Ouipul' should integrate the information provided in 'answer', enriching the content while maintaining medical accuracy.2. 2. The output should be logically coherent and flueatly expressed, and must strictly climinate any potential references to hospital and personal privacy information.
final response should formatted follows::" i"instruction"" " " ""SS"""""S"""SSSSS/SSSS
(UNST]+
"instruction"" "What tests are needed for the Vertigo",
"answer": "The purpose of testing for vertigo to identify the cause of the symptoms, as vertigo can be caused by a variety of issues including inner ear problems, cervical spine issues, and brain discases. Common tests include hearing tests, balance assessments, eye movemnent lests, and intracranial CT scans. However, the specific testing plan should be determined based on the patient's 'ss specific cireumstances (such
duration of symploms, froquency symptorns, accompanyy ing symplomss, etc.). you can provide more information, I will be better able to guide your treatment plan. Additionally, to alleviate symptoms, it is recommended that you rise slowly when getting up or changing positions, avoid excessive mental fatigue, and engage in appropriate physical activity. |<USER+//INST]
JOUTPUT]AASSSTAATS
"instruction". "What detailed dingnostic tests should he performed to confimm vertigo, and what their purposes and specific contexts in which they are used? Pleuse specify whether certain underlying causes, such as inner ear problems, cervical issues, or brain discases. need to be considered and how to choose appropriate tests based on the patient's clinical sympioms. Are there any general recommendations that could help alleviste patient symptoms?"" "output":" The purpese of diagnostic including inner car problems, cervical spine issues, and beain discases. To accurately diagnose vertigo, the following tests are commonly performed:
vertigo identify the underlying cause, vertigo can be triggered by various issucs,
1. I Hearing Test (Audiometry): This helps detect issues related to the inner ear auditory system, conditions like Meniere's disease or acoustic neurema often cause vertigo.
2. Balance These assess vestibular system's function by evaluating the patient's balance. Standard balance tests include the
Vestibular Evoked Myogenic Potentials (VEMP) and Electonystagmograpyy (ENG).
3. Fye Movement Tests: Fllecronyttagnograpy (FNG) involves observing and recording eye movements to identify nystagmus and other abnormalities, which often indicate issues with the vestibular system.
4. Cranial CT Scan MRI: These imaging
used rule brain disorders or intracranial pathologics, such strokes, brain
tumorr, or multiple sclerosis, which
lead vertigo symptomm..
The specific combination of lests should be determined based on the patient's is clinical presentation, including the duration symptoms, froquency, and nccompanying signs. For instance, hearing and balance tests may be prioritized if patient also experiences tinnitus or hearing loss. Conversely, imaging studies like CT scan or MRI may be more warranted the patient has headaches other
neurological signs.
addition to these diagnostic tests, some general recommendations can help alleviate vertigo symptoms: patients should rise slowly from bed or when changing positions, avoid overexertion or prolonged periods mental farigue, and engage in appropriate physical activities to promote overall circulation. These measures can help somewhat mitigate vertigo symptoms' severity." >=ASISTANT=JOUTPUS
Figure 10: Illustration of the GPT-4 prompt template for progressive instruction reconstruction rule. The original Chinese content translated into the corresponding English version to facilitate intuitive understanding.
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JINSTJKSY'S UNSTT<SYS-Pleems S>Please assume the role of an experienced data worker. Your task is to create high-quality medical knowledge texts based on the given instruction data, The specific requirements are as follows:
1. Drawing on the provided dialogue and your extensive knowledge base, compile an informative medical knowledge texts from the perspective of writing an encyclopedia
2. Ensure that the generated content is objectively accurate.
3. The language should be smooth and concise. </SY =SSSST//IST]]
[NSTT/USER>
"instruction":
"What hemophilia?",
"output": "Hemophilia group of genetic bleeding disorders caused by deficiency of certain clotting factors the blood,
leading to severe coagulation disorders. can affect both males and females, but the vast majority patients are male. This includes Hemophilia Hemophilia
Factor deficiency (fomerly known Hemophilia C). The first two are X-linked recessive inherited, while the latter is autosomally inherited as incomplete recessive trait. Hemophilla is most common congenital bleeding. disorder, with bleeding being primary elinical manifestation of discase." ></SER>>/INST]
SOUTPUTJASSISERR
Hemophilia is genetic disorder characterized primarily by the inability properly form blood clots, leading to prolonged blooding. This condition is primarily due the deficiency absence of specific clotiing factors, proteins essential for blood clot formation. The must common lypes of this disorder Hemophilia A and Hemoopiilia B, which involve deficiencies in factor VIII and factor IX. respectively. Hemophilia A about four times commnn than Hemophilia B.
The disorder generally inherited in X-linked recessive pattern, which explains its higher prevalence among males. Fernales be carriers of the condition, and while they usually do exhibit symptoms, form, known as Hemophilia C factor XI deficiency, follows autosomal recessive inheritance patterm, affecting both males and females equally, and generally leads milder symptoms.
still experience bleading episodes, Another
Individuals with hemophilia may experience spontameuus bleeding events or prolonged bleeding alter injuries or surgery. Common complications include bleeding into joints muscles, leading pain and, over time, potentially severe joint damaaee Modern treatment primarily involves replacing the missing clotting factor, either regularly as preventive measure or manage bleeding episodes when they occur.
Diagnosis typically occurs childhood and based on blood tests mensure elotting factor levels. Advances medical science, particularly the development of recombinant clotting factors and gene therapy, continue to improve the quality of life life expectancy for those affocted with hemophilia, [ASSISTANTIIOOUPUT))
Figure 11: Illustration of the GPT-4 prompt template for improving corpus density and breadth. The original Chinese content is translated into the corresponding English version to facilitate intuitive understanding.
[NST]s medical evaluation AT, your task analyze the responses from Models and in a dialogue format. Please
evaluate these responses using following criteria:
Usefulness:
Does the response show relevant pediatric expertise and effectively address the question posed?
2. Correctness:
the information provided by the model free from harmful, misleading, or inaccurate content?
3. Consistency: Does the response maintuin logical coherence and stay contextually relevant throughout?
4. Smoothness: Is the response expressed in a fluent, natural manner that mimors typical human communication?
Based on your assessment of these four key areas, please deterninn which model's response is superior, Conclude with a clear statement, selecting from: 'A is better than B', 'A worse than B', or 'A is equal to (SSSSSNNISST
USST
[Assistant 1]
[The Conversation from Model A]
[End of Assisiant]]
[Assistant 2]
[The Conversation from Model B]
[Fnd of Assistant2]
<USER=//NST]
Figure 12: Illustration of the GPT-4 prompt template for evaluating paired model responses.
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Instruction:
Hi Hi A0I N6 10
(Please summarize the etiology of glucose- 6 phosphate dehydrogenase deficiency blood cells.)
Zhongjing
TA AA 4 d.. SU
21
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(Glucuse- 6 phusphate dehydrogenase de deficiency in red blood cells is a hereditury disorder primurily caused by genetic mulations. These mutations lead to structural and functional abnormalities in the glucose-6-phosphat dchydrogenase eazyme within red blood cells. affecting the synthesis and metabolissm of hemoglobin. As a result, patients experience symptoms such anemia. jaundice, and abnornal liver function.)
HuntuoGPT-II AT Wh
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X 15
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glucose---phosphate phosphate debydrogenase deficiency in red blood cells. These mutations result impaired or complete absence of the function of mucoss-6--hoophaae dehydrogenase, thereby affecting the normal metabolic proceses of rod blood cells. This discase is typically passed down through generations vin genetic inheritance, with the specific mode of inheritance dopending on the type of mutation. Certain environmental factors may also play a role in the onset of dlucos-6-plosphatt dehydrogenuse deficiency, For example, certain drugs, infections, and stressors, among other factors, may trigger discase episodes. In summary, glucose-6-phosphnn dchydrogenase deficiency in red blood cells genetic disorder caused by mutations, primarily resulting in impaired or absent function of glucose-6- --possphtte debydrogenase due to genetic mutations.)
(Genetic mutations are leading cause glucose-6
PediatriesGPT
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E2. $9 le DMM. HE #5 It's M.
28 BI 3 R. BI 12-22 b. o ME TE 19 (R
Gelucosc-6-phoosple dehydrogenase (G6PD) deficiency is hereditary discrder caused by mutations in the G6PD gene. G6PD an
enzyme primarily found in red blood cells and crucial for protecting these cells from oxidative damage. G6PD helps maintain reduced glutathione (GSH) levels, an important autioxidant that neutralises harmful oxidants and protects red blood cells from damage. When the activity of G6PD in the body is reduced or absent, red blood cells become more susceptible to damage under oxidative stress conditions, such as exposure to cerlain foods, medications, infections, or other stressors. This increased susceptibillty can lead to the premature rupture of red blood colls. resulting in hemolytic anemia. Symptoms of this condition can include fatigue, juundice, and dark urine. The inheritance pattern G6PD deficiency is primarily X-linked recassive. Males (XY) have only X chremosome, so they will exhibit the discase if their X chromosome carries the defective G6PD gene. Femules (XX) need to have the defective G6PD gene on both of their X chromosomes to show the disease; if only one X chromesome carries the defective gene, they are typically asymptomatic carieer. though they may sometimes exhibit mild to moderate symptoms G6PD deficiency is widespread globally.
particularly regions such as Africa, the Mediterramean, the Middle East, and parts of Asia. This prevalence is linked high incidence of malaria these regions, as G6PD deficiency offers protection against certain types of malaria.)
historically
Figure 13: Comparison results of model responses on the medical knowledge question-answer task.
22

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Instruction-1:
B 31 91 BO H0s. 2.
(My child's situation is as follows: male, presented "difficulty breathing for over hours" after birth. What could be the cause?)
Instruction-2:
# Be
MM: WBC 24.82x10' RBC 1.13 Ilb 198 gt, 3L8 378x10'9/1, CRP 25mg/1.. III 4
M:: pH 7.20, PaO2 52mmHg. PaCO2 56mmHHg cHCO3- 17.2mmol/LL MA BiE 34 PEBDITIS//BEERR 31
58mmntlg. - al 1BB5=** [] 3.3mm, 9) # L8mmn, S
(The child's auxiliary examination results: Complete blood count: WBC 24.82 x 10"9/L, RBC 8.23 x 10-12/L, Hb 198 g/L. PLT
378 10*9/L, CRP 25mg/L. Blood gas analysis: ptl 7.20, PaO2 52mmiig Pa002 56mmiigg cHCO3- 17. 17.2mmol/L. BBoood culture: negative. Chest X-r Reduced transparency in both lung fields, diflisee fine gramulur shudows, peripheral signs of bronchial inflation.
Echocardiogram: Patent ductus arteriosus 3.3mm, patent foramen ovale 1.8mm, tricuspid regurgitation pressure gradicnt 58mmHg.
Zhongjing
Response-1:
1858. Wit (1 DI MAX 85. WH 2TX
(Hello, may I help you with anything? I'm happy lo ussist you. Is there anything I can help you with?)
)
Response-2:
HR F09H0 mi 1i WEI In MANTE (FMC
W TT dh. C 5 M. 1
(Based on the child's auxiliary examination results, all parameters the complete blood count are within normal ranges. Blood gas analysis nornal, but low cHICO3 suggests child may have acidusis. The chest X-ray shows reduced transparency buth lung fields with visible diffuse fine granular shadows, likely due respiratory system disease. The echocardiogram reveals patent ductus arteriosus, patent foramen ovale, and a high tricuspid regurgitation pressure gradient, necessitating further evaluation and treatmentt
HuatuoGPT-II
Response-1:
X/ 117--22 2524 32 08 th. W -180
(Sorr,, you haven't provided enough information. Please provide some more details.)
Response-2:
#L 65 B... # T N9 of Filhkk
M & (WBC) J.M. CHAREE (CRP) JiM the
T HerT Ae InI M of Tipl ME, PaO2 H. PACO2H155M,
H HE EE H. M Re
165 of WA
of (:B Ch vian Ok 54 AA
B +L
(Based on the information, the child's symptoms suggest be may have pneumonia. Tests show elevated white blood cell count (WBC)
and C-renctive protein (CRP), indicating a possible infection and inflammation. Blond gus analysis results show decrensed pH.
reduced PaO2, and increased PaCO2, suggesting potential respiratory acidosis. The chest X-ray shows reduced transparcncy in both lung fields with visible diffuse fine granular shadows and peripheral signs of bronchial in inflation, indicative pneumoonia.
Fehocardiogram results reveal patent ductus arteriosus and foramen ovale, and an increased tricuspid regurgitation pressure gradient, suggesting possible heart issues. It is recommended to take the child to the hospital promptly for further examination and treatment.)
PediatricsGPT
Response-1:
S53. HL 1 TE I IE RE 2t
(Hello, understand your concern. In order have more comprehensive understanding of your child's condition, I need you provide
some hospital auxiliary examination results.)
Response-2:
HEE (5 BE (T Roll 1E Di
1. 1LA WBC (ÉPEUI))
24.82 x 10.9/L, MO
ff RBC ( 8.23 x 10 #1 Hb ( 198 gl, AA IIL
BErIIA THE & HE
-PLT (I1/))H)) 378 x 10 TE CRP (C 25 mg/., M5TV
A.... 2. Iil Hi: -pH 7.20. PaCO2 56 mmHg. eHCOR- 17.2 mmol/t. Ai fT Mi PPn02 52 mmHg. Wo E HUILEE.
1471 14 SA MIE. M wf RR Ini klll. 5/ X St
TFF TH "Jb Re t2 TO R. C -00 4 R AI MyfE Ai PiE Mt. TI Ti 4 HE
# - 58 miitg. ME 4i ae BDA))
B6 FT 4F BR the A f1-44
* H shikk # API $139 MN A TE 7 BI
2h M. 11 DKIM Hi. Ell 12 dt
1E88 U MI T 1i 4:
A 22 2 C: de 17 ST CEF. FF fl
f1 S HE Wo 251. 1657 Bl
30 1e [e]B]. Mn ui We bO MEC BE Fuui 1850
+ $ 9 IA fl; 1 12
(Based on the provided information, the child's medical ovaluations indicate complex health issucs across multiple systems: I. Complete
Blood Count reveals elevated WBC at 24.82* 10 suggesting infection or inflammation. high RBC and Hb possibly indicating polycythemia. normal PLT. and elevated CRP pointing to inflammation.. 2. Blood Gas Analysis shows a pil of 7.20. elevated PaCO2.
low cHCO3- suggesting metabolic acidosis with respiratory compensation. and low PaO2 indicating hypoxemia. Chest X-ray reveals decreased transparency and diffuse granular shadows, suggesting infection pulmonary edema, with peripheral bronchial inflation signs hinting at chronic lung conditions. 4. Echocardiography shows palent ductus arteriosus and foramen ovale with right-to-lel left shunting, and a tricuspid regurgitation pressure gradient of 58 mmHg indicating significant pulmonary hypertension. These findings suggest lung infection pulmonary edema, metabolic acidosis and respiratory compensation. Patent ductus arteriosus and foramen ovale could cause hemoddynamic issues, including pulmonary hypertension. Inmmediate measures should include monisoring viial signs and exygen levels, respiratory support, antimicrobial therapy, and acid-basc correction. Continuous cardiac monitoring and potential cardiac support are necessary. Coordination with multidiscipiinary team including pediatric cardiology and respiratory specialists is crucial for an integrated treatiment appeouch.)
Figure 14: Comparison results of model responses on the evidence- based diagnosis task.
23

---

Instruction: M AE
(My child fell while playing a few days ago and complained of severe pain in the lower back. After visiting the hospital, the diagnosis was confinmed as lumbar disc herniation. would like know what medication treatment should be adopted for children with lumbar disc herniation?)
Zhongjing:
#3 oI 45 H S8. (E. d Fif P
(Glucoee--phhoopaate dchydrogenase deficiency in red blood cells hereditary disorder primarily caused by genetic mutations. These mulations lead to structural and functional aboermalities in the glucose-6-phe p-poossaap dehydrogenase enzyme within red blood cells, affecting the synthesis and metabolism of hemoglobin. As result, patients experience symptoms such as anemia, jaundice, and
abnormal liver function.)
HPT-:
C ill df Lill L H U. 30
X.
A3557 XT JL
AE th
1. #E (NSAIDs)
if (Ibuprofen) #
(Naproxen) T.
R T
2. HU H:5L.
Im af "T (Amnoodiinne Besylate) T, TT
DEU R 3. N
K1 Biik
EI 4A El AP is SL.
4. t mifde (C 3 1525. TT De
15 de t6 5 FilffT.
excessive activity. Treaiment methods mainly include conservative and surgical treatments. Pharmacological treatment for podiatric lumbar disc hemiation typically includes the following: Nonsteroidal ant-inillammttrry drugs (NSAIDs): such as ibuprofen or naproxen, can help alleviate pain and reduce inflammattion. Muscle relaxants, such as amlodipine besylatee, which can help relieve musele spasans. Neurotrophic drugs like vitamin B complex can help with nerve repair. 4. Traditional Chinese medicine treatment, such blood-activating and stasis- resolving Chinese medicine, can help improve blood circulation and alleviate pain. Pharmacological treatment for children should be conducted under the guidance of a doclor.)
(Lurnuur disc herniation
children and typically associated with skeletal underdevelopinent
PediatriesGPI:
BE DE de 42 M.. OS ME F JL JL tI
LI ES 20 A BBTA
BIdEE (NSAIDs) # (Ibuprofen) Z (Acctaminophen)
TH स (Broomheiie) %. Ti My 62 yi. 3,
B Une WHik
AN RE, M. 4.475t15::: # + WA TI 12 Me
Hf7. 15 di 10 Fo M.. Mt JL 1ES.. 6
X3 39.
ER FHLLi Ii, J HE SE #..
(Lumbur disc herniation treatment mainly includes: I. Nonstcroidal anti-inflammatory drugs (NSAIDs), such as ibuprofen or acctaminophen. can relieve pain and roduce inflammation. 2. Muscle relaxants: For children, commonly used ones include bromhexine. which can help relieve muscle spasms. Neurotrophic drugs: example, vilamin complex improve nerve function and alleviate pain. Traditional Chinese medicine treatment: Selection should be cautious in children, and consideration may be given to some traditional Chinese medicines with bleod-activating and stasis-eesllvig effects. It is important to emphasize that the treatment of lumbar disc herniation in children should be conducted under the guidance al'a professional doctor, and pharmacological treatment should carefully consider dosage and type. Additionally, children sbould exercise, maintain good posture, and avoid prolonged periods looking down at smartphones computers, which can help prevent lumbar disc hernstiion))
in children, but tresiment still requires caution. For pediatric lumbar disc berniation, pharmacological
Figure 15: Comparison results of model responses on the treatment recommendation task.
24

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31


Reviews:
[{"review_id": "2QUPz5qkZr", "reviewer": "Reviewer_oou4", "summary": "This paper proposes the first Chinese pediatric Large Language Model (LLM) assistant, designed through a robust training pipeline including continuous pre-training, full-parameter supervised fine-tuning, and direct following preference optimization. The model is shown to outperform existing Chinese medical LLMs in pediatric tasks, and the authors intend to open-source both the model and dataset for community development.", "questions": "N/A", "limitations": "Yes", "rating": 5, "confidence": 4, "soundness": 3, "presentation": 2, "contribution": 3, "strengths": "- First Chinese Pediatric LLM Assistant with Pediatric Expertise: PediatricsGPT represents a significant milestone as the first Chinese LLM specifically designed to assist with pediatric medical applications, demonstrating expertise in this specialized domain.\n\n- Novel Domain Adaptation Paradigm: The paper introduces an innovative training pipeline that includes continuous pre-training, full-parameter supervised fine-tuning, and preference optimization, effectively adapting the model to the pediatric domain.", "weaknesses": "- Ethical Issues of the Data: The paper lacks explicit details about the usage agreements for the data sources, including license information and open-source details. Given the data leakage issues inherent in LLMs, clarifying these aspects is crucial for the ethical release and use of both the model and dataset.\n\n- Evaluation Datasets: The three main evaluated datasets appear to be private, raising concerns about their accessibility for further research and verification. The paper should clarify whether these datasets can be openly released and if there are any ethic issues of releasing these datasets.\n\n- Privacy Concerns with GPT-4 API: The use of the GPT-4 API to build instruction data introduces potential privacy concerns, particularly regarding whether data from guidelines and knowledge bases can be legally and ethically fed into a commercialized API like GPT-4.\n\n- Missing Representative Baselines: The paper does not include comparisons with leading medical LLMs such as Meditron, Me-LLaMA, and other advanced models like GPT-4, LLaMA3. Including these baselines would provide a more comprehensive evaluation of PediatricsGPT’s performance.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "We thank the reviewer for recognizing our contribution of significant milestone, novel domain adaptation paradigmm, and innovative training pipeline. Below are some specific responses. The common questions regarding the ethical issues can be found in the **global response**.\n\n**Q1**: About the evaluation datasets.\n\n**A1**: Constructive proposal! As stated in line 21, we are committed to open-sourcing datasets and models to advance the community. In this case, three main evaluated datasets will be made public at the same time to support further research and verification. All components of the database to be released are anonymized and desensitized to eliminate any potential ethical issues. Relevant discussions can be found in the **global response**. We promise to improve the open-source details in the revision.\n***\n**Q2**: Privacy concerns with GPT-4 API.\n\n**A2**: We want to clarify several aspects to emphasize that the usage of the GPT-4 API in this study does not pose potential privacy concerns.\n\n* The specialized pediatric data used contains only purely medical knowledge from textbooks, guidelines, and knowledge graphs without any personal or sensitive information. Based on this, we further filtered ambiguous and misleading content through meticulous manual screening to avoid any privacy concerns. As stated in lines 106-108 and Appendix A.1, the proposed role-playing-driven instruction construction rule assembles structured instructions only from the consolidated textual data. This means that our rule eliminates privacy concerns in the input stage of the API. In addition, we restrict the generation behavior of the API to a given secure corpus through carefully crafted prompts, which excludes the generation of potentially unethical content from the internal knowledge of the API.\n\n* The collected conversational data has been approved by the institutional ethics committee, which is a prerequisite for us to access the conversational data. All data are strictly anonymized and de-identified to avoid ethical issues. Each participant signed a GDPR informed consent form, which allows the dataset to be publicly available for research purposes. In this case, our in-context learning strategy only regularizes concise vanilla conversations via the API without introducing privacy concerns.\n\n* For the distilled medical datasets, the original gatherers have anonymized the relevant corpus to ensure no personal information is contained. As stated in lines 123-124 and Appendix A.3, our progressive instruction reconstruction rule only enhances the informativeness and density of medical knowledge in the instructions through the API without any privacy leakage.\n\n* The above data resources comply with OpenAI's official terms and security criteria to allow researchers to use the corresponding API in a legal and ethical manner.\n\n***\n**Q3**: Regarding other representative baselines.\n\n**A3**: Many thanks to the reviewer for the constructive proposal. We clarify the baseline selection and comparison through the following points.\n\n* For a fair and intuitive comparison, the used baselines focus primarily on models that specialize in building Chinese language healthcare capabilities. This means the models' training corpus and response targets are predominantly Chinese rather than English throughout the whole training process.\n\n* We follow the proposal to incorporate the English-oriented baselines to provide comprehensive insights. Among the listed LLMs, we implement models including Meditron-7B-1.0, LLaMA3-8B, and GPT-4. We are considering incorporating Me-LLaMA in the revision as we did not have permission to access its weights before the rebuttal deadline. It is worth noting that the submission deadline (2024/5/22)  for the manuscript was earlier than the release (2024/6/5) of Me-LLaMA's weights.\n\n* Tables 1 in the **Response.pdf** show the comparison results across different tasks on the MedKQ&A, EviDiag, and TreRecom benchmarks, respectively. PediatricsGPT-7B outperforms similarly sized models by large margins, proving the medical expertise of our model. Although other baselines are more competitive on the Distinct metric, which measures response diversity, the precision and recall of responses are more valuable in the medical domain. Furthermore, our 13B version achieves competitive or even better results compared to GPT-4 on the vast majority of metrics, showing the effectiveness of the proposed systematic training pipeline in building domain-specific models.", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "FfNAzaQPq4", "reviewer": "Reviewer_jQHJ", "summary": "The paper presents PediatricsGPT, a specialized large language model (LLM) designed to assist in pediatric medical consultations in China, where there is a significant shortage of healthcare resources. PediatricsGPT is built upon PedCorpus, a high-quality dataset containing over 300,000 instructions from pediatric textbooks, guidelines, and knowledge graphs. The model undergoes a systematic training process that includes continuous pre-training, full-parameter supervised fine-tuning, human preference alignment, and parameter-efficient secondary supervised fine-tuning. This comprehensive approach aims to address the inadequacies of existing Chinese medical LLMs in pediatric applications.", "questions": "See above.", "limitations": "Yes", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "1) The development of PedCorpus, a robust dataset with over 300,000 multi-task instructions from credible pediatric sources, ensures that PediatricsGPT has access to diverse and accurate medical knowledge.\n2) The multi-phase training process, which includes continuous pre-training, full-parameter fine-tuning, and human preference optimization, enhances the model's capability to generate precise responses, and adapt to the complexities of pediatric medical consultations.\n3) Experiments show that PediatricsGPT outperforms other Chinese medical LLMs, validating its effectiveness and reliability in real-world applications.\n4) Manual human study is conducted.", "weaknesses": "1) Overall I believe the developed model with the curated datasets will be a useful resource. But I think the authors can probably better highlight the novelty in this paper and how the techniques employed in this paper differs from other works.\n2) As one of the main contribution of this paper lie in the creation of the dataset, I am not sure if this paper is more suitable for the D&B track in NeurIPS. However, this does not affect my overall rating. \n3) The improvements attributed to the universal expert appear marginal, raising questions about whether the gains justify the additional time and memory costs.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "We thank the reviewer for recognizing our high-quality dataset, multi-phase training process, and comprehensive experiments. We present our detailed responses below.\n\n**Q1**: Highlight the novelty of this paper and how it differs technically from other works.\n\n**A1**: Thanks for the constructive comments. The novelty of this study encompasses both dataset and methodology technique dimensions.\n\nFor the dataset dimension, existing instruction data typically involve vanilla rephrasing of the general medical corpus or aggregation of doctor-like dialogues, which loses the specialization and focus in pediatric applications. More importantly, the current straightforward different round instruction construction paradigms fail to accommodate multi-task healthcare services in real-world scenarios, limiting the model generalization. \n\nIn comparison, the novelties of our PedCorpus derive from three characteristics.\n* Task Diversity: besides containing generalist healthcare data, three application-oriented tasks are considered, including medical question-answer, evidence-based diagnosis, and treatment recommendation.\n* Source Richness: distinct pediatric textbooks, guidelines, and knowledge graph resources provide solid assurance of medical knowledge's accuracy.\n* Instruction Extensibility: vanilla instructions can be readily extended to seed instructions for generating specialized corpora to serve different training phases.\n\nFor the technique dimension, prior methods relied on Supervised Fine-Tuning (SFT) to compensate for medical instruction following capabilities, ignoring the discrepancies between inherent and externally absorbed knowledge within the models. This single pattern causes secondary LLMs to lapse into excessive role-playing rather than understanding. Despite a few attempts in the reinforcement learning from human feedback phases, their performance is restricted by actor-critic instability and online sampling bias.\n\nIn contrast, our technical novelty comes from several points.\n\n* As stated in lines 54-56, we propose a hybrid instruction pre-training mechanism in Continuous Pre-Training (CPT) to bridge the capability weakening due to corpus format discrepancies between the internal and injected medical knowledge of foundation models, facilitating knowledge accumulation and extension.\n* As stated in lines 169-176, we present a stable method for domain-specific LLMs called Direct Following Preference Optimization (DFPO). DFPO utilizes variable changes to formulate the preference loss as a policy function that efficiently optimizes the policy with a simple binary cross-entropy objective. Meanwhile, our method directly regularizes model behavior boundaries in an instruction-following paradigm on medical demonstrations of preferred responses,  facilitating robustness and smoothing of the preference learning.\n* As stated in lines 186-199, we devise a mixture of universal-specific experts strategy to tackle the competency conflict between medical generalist and pediatric expertise in secondary SFT, which strengthens the model's adaptability to distinct downstream tasks. Specific experts master different pediatric tasks by soft routing gating control with noise. Also, a universal expert is consistently activated to prevent general knowledge forgetting and mitigate competency conflict.  \n* As stated in lines 106-107, we propose a role-playing-driven instruction building rule to convert the consolidated textual data into instructions. The proposed rule is effective in generating accurate and reliable instruction knowledge for model training.\n* As stated in lines 123-124, we design a progressive instruction reconstruction rule to distill the sampled instructions to ensure informative and logical model responses. Unlike the traditional self-instruct pattern of using APIs, our method guides APIs to take the perspective of the experienced doctor to complete progressive tasks in the given instruction and answer scenarios. \n***\n**Q2**: About the suitable NeurIPS track for this paper.\n\n**A2**: We thank the reviewer for the overall recognition. As we mentioned in **A1**, this paper presents a high-quality PedCorpus and a systematic training pipeline to address the weaknesses of previous works in both the dataset and modeling methodology. Besides the dataset, our methodological contributions span different stages in the training pipeline to address the corresponding challenges. Specifically, we propose a hybrid instruction pre-training mechanism in the CPT stage to bridge the capability weakening due to corpus format discrepancies between the internal and injected medical knowledge of foundation models. In the intention alignment stage, a DFPO is devised to enhance response robustness and align human preferences. Moreover, we propose a mixture of universal-specific experts strategy to address competition across different pediatric tasks and the conflicts between medical generalization and specialized knowledge in the LoRA-based SFT stage.\n\nWe believe that these contributions make this study suitable for the Main Track.    \n***\n**Q3**: Improvements on universal expert.\n\n**A3**: As stated in line 198, the universal expert aims to prevent general knowledge forgetting. In this case, Table 3 clearly shows that the universal expert on the general healthcare benchmark CMD and webMedQA brings significant improvements of 2% and 2.5% on average under the GPT-4 and Doctor evaluation, respectively. Instead, the expert's marginal gains on the three specific pediatric tasks are justified since it is not its duty to acquire task-specific knowledge. Furthermore, the visualization results in Figure 6(b) provide evidence that the universal expert handles general healthcare.\n\nIt is worth noting that LoRA-based universal expert requires only 0.24% training parameters, implying almost negligible time and memory cost compared to the whole SFT procedure.\n\nWe promise to optimize the above clarifications in the revision.", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "oCTEkT1l22", "reviewer": "Reviewer_DSSd", "summary": "This paper introduces a Chinese pediatric LLM assistant, PediatricsGPT. It follows a standard pretraining and SFT pipeline to incorporate the general medical knowledge schema into the models. Specifically, they optimize the response to enhance the generation of pediatrician-like humanistic responses.", "questions": "1. When designing such an LLM for pediatrics, is there any specific design/technique regarding such a target group, compared with designing a general medical LLM? I understand the preference optimization for the generation of pediatrician-like humanistic responses is one of such designs; however, is there any other specific design in the training/SFT/knowledge integration phase?\n\n2. In Figures 2&3, how consistent is it for each question in GPT and doctor evaluation?", "limitations": "See weakness", "rating": 7, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 4, "strengths": "1. The establishment of a large-scale, high-quality multi-task medical instruction dataset, PedCorpus.\n2. The development of a Chinese pediatric LLM assistant with pediatric expertise and medical generalist, PedatricsGPT.\n3. Open source resources for the community.\n4. Extensive experiments on both domain-specific capability and general medical capability. \n5. Human (expert) study for a comprehensive evaluation", "weaknesses": "1. The authors claim that they crafted 100 high-quality examples to guide the advanced language model, using in-context learning to regularize vanilla conversations in the self-instruct pattern, ensuring doctor-like and patient-friendly model responses. I would like to see a rigorous evaluation of this. \n\n2. According to Table 3, MUE, Universal Expert and RLHF seems to result in a limited performance gain.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "We thank the reviewer for recognizing our large-scale dataset, open-source resources, and extensive experiments. We present our detailed responses below.\n\n**Q1**: About the evaluation of model responses.\n\n**A1**: Insightful comments! To rigorously evaluate whether the in-context learning (ICL) strategy can ensure that the model produces doctor-like and patient-friendly responses, we did our best to invite 100 patients from different departments at our partnering medical institutions to perform the manual evaluation. Each participant was compensated $300, which was greater than the lowest hourly rate in their region. Manual evaluation is reasonable since the response style measure is not easily quantified by metrics but can be readily evaluated by the users. Constrained by rebuttal time, we chose the 7B size to train a candidate version, called CandidateGPT-7B, without using the ICL strategy to regularize vanilla conversations. Other training procedures and settings were kept consistent for a fair comparison. In this case, we asked participants to consider two dimensions to determine the winning response between PediatricsGPT-7B and CandidateGPT-7B, targeting the same instructions on five benchmarks, including doctor-like professionalism and user-friendliness. \n\nTable 2 in the **Response.pdf** shows the average win/tie/loss rates of PediatricsGPT-7B on different benchmarks across distinct evaluators. We found that PediatricsGPT-7B generated more patient-user-friendly response content with higher win rates on pediatric and general-purpose medical tasks. In addition, the lower tie rates on webMedQA and CMD suggest that the doctor-like and patient-friendly response style of our model can be effectively generalized to single/multi-round consultation scenarios.\n***\n**Q2**: Performance gains on MUE, universal expert, and RLHF in Table 3.\n\n**A2**: We clarify the gains of different components separately and provide analyses. \n\n* As stated in lines 193-199, in the MUE strategy, specific experts are adaptively activated to resolve knowledge competition across different pediatric tasks. The consistently activated universal expert aims to acquire general medical knowledge to prevent competency conflict between medical generalists and pediatric expertise mastery. When removing MUE in Table 3, we observe drops of 1.5% and 2% in the average results of GPT-4 and Doctor evaluations on the pediatric benchmarks EviDiag and TreRecom, proving that MUE delivers significant gains. The limited gains on MedKQ&A imply that vanilla single LoRA only masters knowledge question-answer semantics, further reflecting the effectiveness of MUE.\n\n* By observing the setting of removing the universal expert in Table 3, we find that the universal expert brings significant performance gains of 2% and 2.5% on average on the general medical benchmarks CMD and webMedQA under the GPT-4 and Doctor evaluations, respectively. In contrast, the expert's limited gains on the three specific pediatric tasks are justified since it is not its duty to acquire task-specific knowledge.\n\n* As stated in lines 169-176, we propose the Direct Following Preference Optimization (DFPO) to solve the unstable reward modeling and high computational costs in RLHF. In this case, the experiments on RLHF in Table 3 belong to the ablation studies that verify the proposed preference optimization approach. The limited gains of RLHF precisely show the merits and necessity of our DFPO in human preference alignment.\n***\n**Q3**: Other designs/techniques for pediatric LLMs.\n\n**A3**: In addition to the DFPO technique for generating pediatrician-like responses, our study has four other novel designs aimed at the target group.\n\n* As stated in lines 131-138, we propose a hybrid instruction pre-training mechanism in Continuous Pre-Training (CPT) to bridge the capability weakening due to corpus format discrepancies between the internal and injected pediatric knowledge of foundation models, facilitating knowledge accumulation and extension. Our mechanism better facilitates the adaptation of model capabilities in pediatric healthcare.\n* As stated in lines 186-199, we devise a mixture of universal-specific experts strategy to tackle the competency conflict between medical generalist and pediatric expertise in secondary SFT, which strengthens the model's adaptability to distinct downstream tasks. Specific experts master different pediatric tasks by soft routing gating control with noise. Also, a universal expert is consistently activated to prevent general knowledge forgetting and mitigate competency conflict. Our strategy enables our models to perform more comprehensively and competitively in pediatric medical applications.\n* As stated in lines 106-107, we present a role-playing-driven instruction building rule to convert the consolidated textual data into well-designed pediatric instructions. The proposed approach endows the advanced language model with the professional role of an expert pediatrician to generate accurate instruction knowledge for the target model training.\n* As stated in lines 123-124, we design a progressive instruction reconstruction rule to distill the sampled instructions to ensure informative pediatric model responses. Unlike the traditional self-instruct pattern of using APIs, our rule guides APIs to take the perspective of the experienced pediatrician to complete progressive refinement tasks in the given instruction and answer scenarios. \n***\n**Q4**: Consistency of GPT-4 and Doctor Evaluations.\n\n**A4**: We measure the consistency of the results of two evaluations by Bland-Altman Analysis (BAA) and Pearson Correlation Coefficients (PCC). The mean difference value in BAA is 0.21, and most measurement pairs are within the limits of agreement [-0.78, 1.18], suggesting high agreement between the two evaluations. In addition, the PCC score is 0.93, implying high evaluation consistency. We promise to add the above analyses to the revision.", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "oZvkqTMqrl", "reviewer": "Reviewer_eZYU", "summary": "This paper tried to build an AI-powered pediatric consultation system. Their motivation is current Chinese conversation LLMs for healthcare underperform in pediatric applications due to insufficient instruction data and bad training procedures. To tackle this problem, the authors proposed PedCorpus, a high-quality dataset with over 300K multi-task instructions, and PediatricsGPT, the first pediatric LLM assistant in Chinese. The PediatricsGPT baseline models, enhanced with a hybrid instruction pre-training mechanism, supervised fine-tuning, preference optimization, and a strategy to balance general and pediatric expertise, consistently outperforms previous Chinese conversation LLMs.", "questions": "You can check the Weaknesses part and try to answer the question proposed in each item.      \n\nAs a physician, I agree this paper has enough novelty and can serve as a useful foundation model in the future. Thus I tend to give Borderline to this paper before reading the rebuttal. However, there are still plenty of issues that need the author to solve them.      \n\nDuring the rebuttal, the author needs to solve the lacking issue of IRB Approval first. If IRB approval is not attached, the dataset and model may contain ethical issues and can not be released.       \n\nI am also curious how ChatGPT achieves good medical and pediatric QA performance (only slightly lower than the proposed PediatricsGPT model) with in-context learning.", "limitations": "The main limitation is lacking IRB Approval. And most of the data do not contain real pediatrics AI challenges. This dataset can only be considered as a supplement of current released medical QA data for adults in China such as DISC-MedLLM.", "rating": 4, "confidence": 4, "soundness": 3, "presentation": 2, "contribution": 3, "strengths": "- The authors are inspired by the new progress in LLMs and then build new datasets for Pediatrics application and new domain-specific LLM model weights for this task.    \n\n- The paper's structure is clear and the authors propose a rigorous evaluation pipeline.", "weaknesses": "- \"Direct Following Preference Optimization (DFPO) in human intention alignment is devised to enhance response robustness and align human preferences.\" - where is the evidence for this? It would be better to visualize some cases other than using ablation study as we can not see a huge performance difference.\n\n- \"ternary instances in the knowledge graphs\" How to build the ternary instance for your task.\n\n- \"complementary resources\" What is a complementary resource?\n\n- Not sure how to protect patient privacy in your Real Doctor-patient Conversations subset.     \n\n- As a pediatrician, I don't think the current model has \"competence in different pediatric and general healthcare service scenarios\". I review your data in the supplementary, it seems most of data do not contain real pediatrics AI challenge. To prove the generalization of the proposed model or prove the value of the dataset in Pediatrics, you should consider building datasets with more specialized pediatric diseases such as autism, cerebral palsy...     \n\n- It is also important to test the performance of proposed model in other Out-of-Domain dataset, such as other medical QA dataset for adult.     \n\n- It is confusing in your dataset building process. Why choose “expert pediatrician” GPT-4 but not real pediatrician to help you construct the dataset?\n\n- \"doctor-like and patient-friendly\" - how to define doctor-like and patient-friendly. Do you mean 'professional' here?\n\n- The scale of user study is limited. You state \"We invite three doctors (each paid $300) to determine the winner of pairwise models by the majority voting rule.\" However, it would be better to conduct a wide user study from pediatricians as three people can not represent the human preference of the whole group.    \n\n- Institutional Review Board (IRB) Approval is needed as this paper has human user study and the released data includes real doctor-patient conversations.    \n\n- Some minor issues:\n\"Chinese medicine\" can be replaced by healthcare service in Chinese as its clinical meaning can be found here: https://www.hopkinsmedicine.org/health/wellness-and-prevention/chinese-medicine", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "Insightful comments!  Below are some specific responses. The common questions regarding the ethical issues can be found in the **global response**.\n\n**Q1**: Evidence on the effectiveness of DFPO.\n\n**A1**: We provide evidence both quantitatively and qualitatively.\n\n* Since the degree of preference alignment is difficult to measure with traditional metrics, we evaluated it employing the GPT-4 and Doctor by considering different response dimensions. From Table 3, when DFPO is removed, the model exhibits an average 2.8\\% performance degradation across five different benchmarks in Doctor evaluations, specifically a 4\\% drop on the webMedQA. These observations prove that DFPO can provide a user-friendly response style that aligns with human preference in expert studies.\n\n* We provide a comparison of the model response with and without DFPO for the same query. We show the English-translated version of the Chinese response in Figure 2 of the **Response.pdf**. The response content is partially omitted due to space constraints. We observe that DFPO helps the model to correct the “no specific treatment” hallucination and provide informative content, leading to better robustness. Moreover, the model response after DFPO is more caring and humanistic, in line with human preferences. We promise to add the qualitative analysis to the revision.\n***\n**Q2**: How to build ternary instances?\n\n**A2**: The sequential steps are as follows:\n\n* Structured knowledge is extracted from the consolidated textual database using natural language processing methods. Specifically, we used named entity recognition and relationship extraction techniques to identify medical entities (e.g., diseases, symptoms, and medications) and their relationships from the text.\n\n* Then, we assemble ternary instances in the knowledge graph, which contains three parts: subject, predicate, and object. \n\n* We ensure the accuracy of ternary instances through expert validation and manual scrutiny to provide a solid foundation of medical knowledge for our task.\n***\n**Q3**: What is a complementary resource?\n\n**A3**: Since corpora from textbooks, guidelines, and knowledge graphs have different characteristics, they provide complementary resources for pediatric data extraction. Specifically, textbooks contain authoritative disease content from basic to advanced levels, providing systematic knowledge structures. Guidelines focus on hands-on and diagnostic applications, providing knowledge of specific treatment recommendations. Knowledge graphs contain hierarchical correlation semantics between diseases and symptoms, providing evidence-based diagnostic knowledge. \n***\n**Q4**: Regarding the model generalization and the dataset value in pediatrics.\n\n**A4**: We want to clarify two points.\n* As shown in Figures 4\\&5, we performed extensive comparisons with other baselines on the adult general medical benchmarks CMD and webMedQA, which contain QA data from different departments. These benchmarks belong to the OOD test data since they are not included in the training set. From lines 257-264, our model outperforms previous LLMs in most departments, proving robust generalizability.\n\n* From line 103, PedCorpus incorporates 131 pediatric diseases from 11 broad categories from textbooks to provide specialized pediatric knowledge. From Table 1, PedCorpus also includes data from multiple medical departments to supplement general medical knowledge. In this case, the cases in Supplementary are randomly sampled from the mixed data only to show the instruction construction process. We will open-source pediatric and general medical data separately to provide different values to subsequent studies.\n***\n**Q5**: Reasons for choosing GPT-4 construct data.\n\n**A5**: We want to clarify that hiring extensive pediatricians working to build large-scale datasets from scratch is labor-intensive and cost-expensive. It is a mainstream and effective strategy to use the advanced GPT-4 that can match human preferences well in a role-playing manner. However, we employed several pediatricians to participate in the instruction checking to ensure the professionalism of the datasets.\n***\n**Q6**: About doctor-like and patient-friendly.\n\n**A6**: \"Doctor-like\" means that the response contains professional content like the physician's. \"Patient-friendly\" means that the response is well-presented, logical, and informative, making it easy for the user to understand.\n***\n**Q7**: About the user study.\n\n**A7**: With limited rebuttal time, we did our best to conduct a user study covering 50 participants (each paid $300) in Figure 1 of the **Response.pdf**. Average evaluation results from participants are reported on three benchmarks. We observe similar performance trends as in the original user study from Figure 3, implying the superiority of our model. Furthermore, we evaluated the consistency of the results from the two user studies using Pearson Correlation Coefficients (PCC). The PCC score of 0.89 indicates the effectiveness of the small-scale user study. We promise to optimize the related analysis in the revision.\n***\n**Q8**: About \"Chinese medicine\".\n\n**A8**: We thank the reviewer for the constructive proposal. We promise to fix it in the revision to avoid the meaning confusion.\n***\n**Q9**: In-context learning (ICL) for ChatGPT.\n\n**A9**: Insightful comment. We designed a semantics-driven strategy to select suitable examples to guide ChatGPT in improving performance by ICL. During testing, we randomly sample 100 samples from the training instructions to compute the cosine similarity of feature semantics with the test sample. The features are extracted by the BERT model. The training samples with similarity scores ranked top-5 were then used as ICL examples to guide ChatGPT in performing generation. Table 3 in the **Response.pdf** shows the potential of ICL to improve the ChatGPT. However, our model still outperforms ChatGPT on most metrics due to healthcare-specific systematic training.", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "UE6w6RdJxa", "reviewer": "Reviewer_eEmB", "summary": "- This paper introduces PediatricsGPT, a Chinese AI assistant for paediatrics\n- They created a large dataset (PedCorpus) with 300k+ medical instructions\n- The training process is pretty involved - includes pre-training, fine-tuning, and preference alignment\n- They came up with some new techniques, like hybrid instruction pre-training\n- Evaluation was thorough - used metrics, GPT-4, and even had real doctors test it\n- Results show it outperforms other Chinese medical AI models in various health tasks, as well as ChatGPT\n- The authors discuss its potential to support doctors and improve pediatric care", "questions": "Line 12: Immediately, the... Please rephrase, a bit hard to understand\n\nLine 29: Please either reference your own results, or cite a resource that supports your claim.\n\nLine 101: Please add information on how was this done, how were the books selected and how was the knowledge automatically extracted. \n\nLine 109: Given that real patient conversations were used, it would be beneficial to know from where exactly was this data obtained, and were the appropriate ethical guideliens followed. \n\nLine 107: Manually sampled - does this mean a human went through everything and selected the appropriate data.\n\nLine 164-168\n- PedCorpus-DFPO is selectively sampled from vanilla PedCorpus - What does it mean \"selectively sampled\", how was this done? Were there any biases? What parts of PedCorpus were sampled and in which proportion?\n- Humanistic stylistic rephrasing - what exactly is this? Who performed the rephrasing (humans or AI, or a combination)? What guidelines were used? How was the quality and consistency of the rephrasing ensured? \n- Low-capability medical assistant - please add a bit more info on why was the HuaTuo model chosen, and why is it low capability (how was this verified).\n\nLine 208: How were the 208 difficult tasks sampled, and how was difficulty determined?\n\nThe paper would benefit (especially the first couple of pages) from a review of the grammar and sentence clarity.\n\nNote: most of the datasets are in Chinese, maybe some information was obvious, but because of my lack of understanding of the language I was unable to determine that.", "limitations": "A very limited validation on real-world use cases, this is more of an initial research paper that can be built upon and further tested in the real world. Would be great to address this in the main part of the paper and not an appendix.", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "- It's tackling a real problem - the shortage of pediatric care in China\n- The dataset they built (PedCorpus) is very comprehensive and high-quality\n- Their evaluation is thorough, using multiple methods including real doctors\n- The model outperforms existing Chinese medical AIs, which is impressive", "weaknesses": "Overall the paper would benefit from more information on dataset construction, for each specific task and in general. In many cases, it is not clear what is meant by manual sampling, automatic extraction, dataset collection and similar. \n\nIt would be great to see the standard deviation for the reported results.\n\nIt would be great to see a comparison with human experts, but this is too much to ask for now, so future work.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "We thank the reviewer for recognizing our comprehensive datasets, new techniques, and thorough evaluations. We present our detailed responses below. The common questions regarding the ethical issues can be found in the **global response**.\n\n**Q1**: Explain the dataset construction including textbook selection, automatic extraction, and manual sampling.      \n\n**A1**: \nValuable proposal! We provide detailed explanations based on three parts: \n\n* As stated in lines 101-108, pediatric task-specific data is derived from specialized textbooks, guidelines, and knowledge graphs. In this case, specialized physicians were invited to select valuable textbooks and guidelines from the candidate books. The selection criteria consist of checking the corpus for the presence of informative medical knowledge and up-to-date healthcare content. \n* Suitable natural language processing techniques are used to accomplish the automated extraction process, including named entity recognition (NER) and syntactic parsing. Specifically, we utilize the NLTK tool to extract vital medical terms (e.g., disease names, symptoms, and drug names) from the text. The Stanford Parser-based dependency parsing model is used to analyze sentence structure to find relations among valid medical terms. These techniques select knowledge-intensive passages from the streaming corpus for subsequent instruction data construction.\n* As stated in lines 117-124, the general medical data is mainly from distilled medical datasets. In this case, manual sampling means that we first thoroughly check the completeness of the instructions by considering multiple dimensions, including whether the instructions contain meaningful symptoms and treatments. Instructions are removed if they miss critical information. Next, we employ a BERT-based semantic similarity model to check the instructions information density and select informative parts.\n\nThe above procedures help us select appropriate data to construct high-quality PedCorpus. We promise to incorporate the above details in the revision to provide comprehensive insights.\n***\n**Q2**: Providing the standard deviation of the reported results.\n\n**A2**: Table 4 of the **Response.pdf** provides the mean ± standard deviation of PediatricsGPT results on different benchmarks. We find that the deviations of our model under different metrics across three tasks are within the range of ± 0.8, with most of the deviations of the 13B results in the much smaller interval of ± 0.5. These observations confirm the consistency and effectiveness of the experimental results. We promise to add the above analysis to the revision.\n***\n**Q3**: Comparison with human experts.\n\n**A3**: We fully agree with the reviewer's proposal because incorporating results from experts helps provide comprehensive insights. To this end, we made the best effort to hire ten pediatricians ($300 each) in limited rebuttals to provide human responses corresponding to test queries on different benchmarks. To better evaluate using ground truth-based metrics, we declared evaluation protocols and required responses to be as well-presented and logical as possible. Table 4 of the **Response.pdf** shows the average results from the human experts. We found that experts performed best on the EviDiag, which requires multi-round consultations. This makes sense since pediatricians have more experience with interactive diagnosis in their practice than the models. The superior performance of our model on knowledge Q&A and treatment recommendation confirms its potential to serve pediatric services.\n\nWe are committed to adding expert results to the revision.\n***\n**Q4**: Description of line 12.\n\n**A4**: Constructive suggestion. We will rewrite the original representation in the revision as \"After that, we utilize the full-parameter Supervised Fine-Tuning (SFT) to incorporate the general medical knowledge schema into the models.\" for intuitive understanding.\n***\n**Q5**: Reference to line 29.\n\n**A5**: Thanks for the reminder. We promise to reference our own results in the revision to support the stated claim.\n***\n**Q6**: Related issues of PedCorpus-DFPO.\n\n**A6**: \n* To ensure that the DFPO phase aligns user preferences in different instruction tasks and genres, selective sampling refers to sampling 3889, 3889, 3889, and 3889 instructions from pediatric data, real conversations, Huatuo-26M, and MedDialog, respectively. We extract the original instruction features in these four parts by BERT. Then, K-means clustering is used to identify the instruction cluster features through the optimal 3889 clusters. We select the closest instruction in each cluster as the sampled instruction to avoid data bias.\n\n* Humanistic stylistic rephrasing refers to making the responses as caring and well-presented as physicians. The process is performed by specialized physicians to ensure quality.\n\n* Huatuo (i.e., BenTsao) is chosen to generate low responses because: its performance was the worst on multiple medical tasks in the latest previous study [1]; and we find in practice that its responses were often hallucinatory and inaccurate.\n***\n**Q7**: Difficult task sampling of line 208.\n\n**A7**: We invited an expert physician from each of the six medical departments to manually sample 50 difficult instances. Difficult tasks were defined by considering two dimensions, including whether they contained rare diseases and whether they required complex logical reasoning.\n***\n**Q8**: Review of the first couple of pages.\n\n**A8**: Constructive advice. We have reviewed the grammar and sentences of the first couple of pages and promise to fix them in the revision.\n***\n**Q9**: About real-world use cases.\n\n**A9**: We promise to add more real-world use cases to the main part of the extra page after acceptance.\n\n[1] Yang, S., Zhao, H., Zhu, S. Zhongjing: Enhancing the Chinese medical capabilities of large language model through expert feedback. In AAAI (Vol. 38, No. 17, pp. 19368-19376).", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "MsYi9XIqzR", "reviewer": "Reviewer_MG71", "summary": "The authors of this paper built PedCorpus, a Chinese pediatric dataset, and PedistrcsGPT, the first Chinese pediatric LLM assistant. Their model was built via continuous pertaining, full-parameter supervised fine-tuning (SFT), direct following preference optimization, and parameter-efficient secondary SFT. Performance evaluation results show that their model outperforms other models.", "questions": "1. Do you think DFPO performs better only on your specific task or can generally perform better than RHLF and vanilla DPO? If it can generally perform better, then this can be a great contribution.\n2. How will you deploy your model in practice to complement the current shortage of pediatric healthcare resources?", "limitations": "The authors list two limitations, which make sense. I wonder whether your model can be 100% accurate. If it generates errors or hallucinations, it should be important to find them and avoid showing them to patients and their parents. As your model will be used for medicine and children, this should be more important than the LLM applied to other fields. This again goes back to my question before: how will you deploy your model in practice?", "rating": 7, "confidence": 4, "soundness": 4, "presentation": 4, "contribution": 3, "strengths": "Originality: This is the first time that Chinese LLM has been applied to pediatrics. The author also proposed DFPO, which improves the performance of preference optimization.\n\nQuality and Clarity: Generally, the paper clearly presents the datasets, the methodology, and the performance evaluation, including the experiment settings and results.\n\nSignificance: The model could complement the current shortage of healthcare resources in pediatrics.", "weaknesses": "1. The techniques in this paper are not that novel, as it applies LLM to another field with main techniques either not original or adapted from others (e.g., DFPO). \n2. The paper focuses on the application level. I believe the model could be important if it is deployed. However, there is no evaluation from real users (patients or patients' parents). Doctor's evaluations could be different from the patients' evaluations, especially since this model will be used for children. This weakens this paper's contributions. I think the authors should at least discuss their plan of deployment or intervention as future work.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "We thank the reviewer for recognizing our new preference optimization and thorough evaluations. We present our detailed responses below.\n\n**Q1**: Clarification of technical novelty.\n\n**A1**: As stated in lines 33-43, this paper addresses the shortcomings at the dataset and framework level of the LLM construction in the Chinese healthcare domain. We propose a high-quality dataset PedCorpus through a series of new instruction-building mechanisms and a systematic training pipeline through new strategies to improve the different challenges in multi-stage training. Our technical novelty is distributed among these mechanisms and strategies. We clarify other contributions besides DFPO mentioned by the reviewer as follows.\n* As stated in lines 54-56, we propose a hybrid instruction pre-training strategy in Continuous Pre-Training (CPT) to bridge the capability weakening due to corpus format discrepancies between the internal and injected medical knowledge of foundation models, facilitating knowledge accumulation and extension.\n* As stated in lines 186-199, we devise a mixture of universal-specific experts strategy to tackle the competency conflict between medical generalist and pediatric expertise in secondary SFT, which strengthens the model's adaptability to distinct downstream tasks. Specific experts master different pediatric tasks by soft routing gating control with noise. Also, a universal expert is consistently activated to prevent general knowledge forgetting and mitigate competency conflict.\n* As stated in lines 106-107, we present a role-playing-driven instruction building mechanism to convert the consolidated textual data into well-designed pediatric instructions. The proposed approach endows the advanced language model with the professional role of an expert pediatrician to generate accurate instruction knowledge for the target model training.\n* As stated in lines 123-124, we design a progressive instruction reconstruction mechanism to distill the sampled instructions to ensure informative pediatric model responses. Unlike the traditional self-instruct pattern of using APIs, our mechanism guides APIs to take the perspective of the experienced pediatrician to complete progressive refinement tasks in the given instruction and answer scenarios. \n\nIt is worth noting that these technologies are domain-agnostic and can be absorbed into other communities' LLM constructions to facilitate long-term developments.                                   \n***\n**Q2**: About the user study and future plan.\n\n**A2**: Constructive proposal. We offer the following two discussions.\n\n**User Study**：With limited rebuttal time and available resources, we did our best to conduct a user study covering 50 patients (each paid $300) in Figure 1 of the **Response.pdf**. Average evaluation results from participants are reported on three benchmarks. We observe similar performance trends as in the original user study from Figure 3, implying the superiority of our model. To investigate the differences between doctor and patient evaluations, we evaluated the consistency of the results from the two user studies using Pearson Correlation Coefficients (PCC). The high PCC score of 0.89 shows that doctor evaluations can reflect patients' preferred outcomes to some extent, suggesting the reasonableness of the evaluation pattern in the main manuscript. The potential reason for this is that patient judgments about personalized medical diagnoses stem largely from interactive knowledge consultations with doctors, resulting in their behaviors being impacted by doctor preferences.\n\n**Future Plan**: We plan to conduct randomized controlled trials with patients from more diverse pediatric medical departments to verify the effectiveness of our model in real-world applications.\n\nWe promise to add the above analyses to the revision.\n***\n**Q3**: Does DFPO perform better on specific or general tasks?\n\n**A3**: In Table 3 of the manuscript, we conducted ablation studies on five benchmarks to investigate the DFPO performance. These benchmarks include three pediatric-specific datasets and two general-purpose datasets with different medical departments. We found that DFPO leads to average gains of 1.4\\% and 2.7\\% in GPT-4 and doctor evaluations on pediatric tasks, respectively. It can bring average gains of 1.5\\% and 2.5\\% in GPT-4 and doctor evaluations on general tasks, respectively. In addition, both vanilla DPO and RLHF perform less well than DFPO in the general tasks. These findings confirm that DFPO performs better overall, leading to superior human preference alignment.\n***\n**Q4**: How to deploy the model in practice?\n\n**A4**: We will adopt two deployment methods.\n\n**Offline Terminal Deployment**: We will deploy smart terminals equipped with our models in our partner healthcare institutions to provide efficient and accurate medical diagnosis and treatment services to patients.\n\n**Online Application Deployment**: We plan to package our modeling system into an application that is easy for users to access on mobile devices. Our application will provide services such as online consultation and consultation record inquiry to reduce the pressure of offline treatment.\n***\n**Q5**: Regarding the accuracy of the model.\n\n**A5**: We emphasize that no medical LLMs can have 100% accuracy in practice. To avoid errors and hallucinations, we are committed to performing the following measures in our deployments.\n\n* We will combine transformer-based representation editing techniques to manipulate the model to generate harmless responses in the factual semantic space.\n* We will add contrastive decoding strategies to reduce the probability of generating unfaithful content for the next token prediction.\n* We will introduce the retrieval-augmented generation to improve the trustworthiness of medical answers.\n* We plan to build LLM-based agents using external tools for verifiable factual knowledge to ensure response security with real-time interventions.", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:
We thank all reviewers for their time and effort. Here we clarify common questions about ethical issues and open source details of the datasets.

**Q1**: Ethical issues with datasets.

**A1**: As stated in lines 101-124, the proposed PedCorpus consists of three parts, including pediatric data, real doctor-patient conversations, and distilled medical datasets. 
* Among these, pediatric data contains only pure medical knowledge from textbooks, guidelines, and knowledge graphs without any private and sensitive information. These resources follow the CC BY 4.0 protocol, which allows users to freely adapt and convert the original data. The creators of these resources are reasonably attributed as co-authors of this study.
* The data source for conversations comes from two components: voice transcriptions collected at our partnering healthcare institution and the publicly accessible dataset cMedQA2 [1]. For the former, we have obtained the approval of the institutional ethics committee, which is a **prerequisite for our access to conversation data**. All data are rigorously anonymized and desensitized to avoid ethical issues. Note that we underwent the internal ethical review by the corresponding Chinese healthcare institution, which is fully compliant with the ethical guidelines of NeurIPS 2024. The corresponding guidelines are described below: **"In cases when no formal process exists, they can undergo an equivalent informal process (e.g., via their peers or an internal ethics review."** Considering the double-blind review policy, we do not have the right to submit the original approval during the review period. If NeurIPS organizers provide special access, we can submit the approval immediately for review. For the cMedQA2, the publisher collected real conversations from online treatment platforms and anonymized personal information. This dataset is licensed under the GPL-3.0 license, which allows users to modify, distribute, and make private use of it.
* In the distilled medical datasets, the protocols for Huatuo-26M, MedDialog, and CMeKG are Apache-2.0, MIT-license, and MIT-license, respectively. These protocols allow users to manipulate the data without restriction, including, but not limited to, the right to use, copy, modify, merge, and distribute. These datasets have been made to protect against privacy leakage. In this case, our instruction reconstruction rule is only used to enhance the density of medical knowledge in the vanilla data.
To summarize, all involved data undergoes rigorous review and processing to comply with ethical guidelines. Our commitment to data privacy and security is unwavering. We promise to refine the usage agreements for the data sources in the revision to provide comprehensive details.

[1] Zhang, Sheng, et al. "Multi-scale attentive interaction networks for chinese medical question answer selection." IEEE Access 6 (2018): 74061-74071.
***
**Q2**: About open source details.

**A2**: As stated in line 21, our database and models will be open source following the MIT license to promote the development of the community. In this case, we promise to release datasets containing training data for model capacity construction and evaluation benchmarks from different tasks and medical departments to avoid researcher concerns about subsequent studies and verifications.
***
**Q3**: Rejection of data leakage in LLMs.

**A3**: This study followed three guidelines to avoid data leakage concerns inherent in LLMs. 
* We carefully verified the pre-training details and data usage of the foundation models to exclude any potential leakage of assessment data in the initial model training. 
* As indicated in line 206, the data in the designed pediatric medical evaluation benchmarks are derived from held-out samples to reject any overlap with the training data in the systematic pipeline. 
* For the general medical proficiency evaluations, we explicitly excluded the used webMedQA and CMD benchmarks from the training data to ensure the fairness and effectiveness of the assessments.

Meta-review: The paper presents PediatricsGPT, the Chinese pediatric Large Language Model assistant, which demonstrates expertise in the specialized domain of pediatrics. The authors propose a novel domain adaptation paradigm and training pipeline, including continuous pre-training, full-parameter supervised fine-tuning, and preference optimization.

The reviewers generally acknowledge the significance of this work as a milestone in Chinese pediatric LLMs and appreciate the innovative training approach. However, several concerns were raised, primarily regarding ethical issues, data privacy, and evaluation methodology.

The main ethical concern was the lack of explicit details about data usage agreements and IRB approval for human subject research. In their rebuttal, the authors clarified that they underwent an internal ethical review by a Chinese healthcare institution, which complies with NeurIPS 2024 guidelines. They also explained that all data sources have appropriate licenses and have been anonymized and desensitized to protect privacy.

Regarding the evaluation datasets, reviewers expressed concerns about their accessibility for further research and verification. The authors addressed this by committing to open-source both the model and datasets under the MIT license, ensuring that the research community can access and verify their work.

The use of the GPT-4 API for instruction data generation raised privacy concerns. The authors provided a detailed explanation of their data handling process, emphasizing that only non-sensitive medical knowledge was used and that strict measures were taken to prevent privacy leakage.

Some reviewers noted the absence of comparisons with leading medical LLMs like Meditron, Me-LLaMA, and GPT-4. The authors acknowledged this limitation and committed to incorporating these baselines in their revised evaluation.

After considering the reviews, rebuttal, and overall contribution of the work, I recommend accepting this paper. The authors have adequately addressed the main concerns raised by reviewers, and the work represents a significant contribution to the field of pediatric LLMs in Chinese. However, I strongly advise the authors to include the promised additional baselines and provide more detailed information about data usage agreements and ethical approvals in the final version of the paper.
Decision: Accept (poster)