MDAgents: An Adaptive Collaboration of LLMs for Medical Decision-Making

Authors: yubin  kim
chanwoo  park
hyewon  jeong
yik siu chan
xuhai  xu
daniel  mcduff
hyeonhoon  lee
marzyeh  ghassemi
cynthia  breazeal
hae won park
Venue: NeurIPS_cc_2024_Conference
Published: 2024-05-08 20:31:50

Abstract: Foundation models are becoming valuable tools in medicine. Yet despite their promise, the best way to leverage Large Language Models (LLMs) in complex medical tasks remains an open question. We introduce a novel multi-agent framework, named **M**edical **D**ecision-making **Agents** (**MDAgents**) that helps to address this gap by automatically assigning a collaboration structure to a team of LLMs. The assigned solo or group collaboration structure is tailored to the medical task at hand, a simple emulation inspired by the way real-world medical decision-making processes are adapted to tasks of different complexities. We evaluate our framework and baseline methods using state-of-the-art LLMs across a suite of real-world medical knowledge and clinical diagnosis benchmarks, including a comparison of
LLMs’ medical complexity classification against human physicians. MDAgents achieved the **best performance in seven out of ten** benchmarks on tasks requiring an understanding of medical knowledge and multi-modal reasoning, showing a significant **improvement of up to 4.2\%** ($p$ < 0.05) compared to previous methods' best performances. Ablation studies reveal that MDAgents effectively determines medical complexity to optimize for efficiency and accuracy across diverse medical tasks. Notably, the combination of moderator review and external medical knowledge in group collaboration resulted in an average accuracy **improvement of 11.8\%**. Our code can be found at https://github.com/mitmedialab/MDAgents.

Main paper: 
 introduction: 
 Medical Decision-Making (MDM) is a multifaceted and intricate process in which clinicians collaboratively navigate diverse sources of information to reach a precise and specific conclusion [97] . For instance, a primary care physician (PCP) may refer a patient to a specialist when faced with a complex case, or a patient visiting the emergency department or urgent care might be triaged and then directed to a specialist for further evaluation [5, 54] . MDM involves interpreting complex and multi-modal data, such as imaging, electronic health records (EHR), physiological signals, and genetic information, while rapidly integrating new medical research into clinical practice [68, 78] . Recently, Large Language Models (LLMs) have shown potential for AI support in MDM [22, 37, 48, 64, 72, 90] . It is known that they are able to process and synthesize large volumes of medical literature [74] and clinical information [1] , as well as support probabilistic [94] and causal [39] reasoning, makes LLMs promising tools. However, there is no silver bullet in medical applications that require careful design.♮ Hyewon Jeong received her MD degree from Yonsei University College of Medicine, South Korea. 2 Appendix F contains a detailed comparison results between human physicians and LLMs.38th Conference on Neural Information Processing Systems (NeurIPS 2024). Given a medical query from different medical datasets, the framework performs 1) medical complexity check, 2) recruitment, 3) analysis and synthesis, and 4) decision-making steps.While decision-making tools including multi-agent LLMs [11, 86] have shown promise in nonmedical domains [31, 32, 44, 46, 62, 65] , their evaluation in health applications has been limited. To date, their "generalist" design has not effectively integrated the real-world systematic MDM process [57] which requires an adaptive, collaborative, and tiered approach. Clinicians consider the current and past history of the patient, available evidence from medical literature, and their domain expertise and experience [20] for MDM. One example of MDM is to triage patients in emergency room based on the severity and complexity of their medical conditions [12, 26, 87] . Patients with pathognomonic, single uncomplicated acute conditions, or stable chronic conditions that their PCP could manage [85] could be low complexity cases. On the other hand, patients with injuries that involve multiple organs, chronic conditions with side effects, or superimposed diseases who often require multiple collaborative discussions (MDT) or sequential consultations (ICT) among specialty physicians [27, 61] are considered high complexity cases 3 . Inspired by the way that clinicians make decisions in practice, we propose Medical Decision-making Agents (MDAgents), an adaptive medical decision-making framework that leverages LLMs to emulate the hierarchical diagnosis procedures ranging from individual clinicians to collaborative clinician teams (Figure 1 ). MDAgents work in three steps: 1) Medical complexity check; 2) Recruitment based on medical complexity; 3) Analysis and synthesis and 4) Final decision-making to return the answer. Our contributions are threefold: 1. We introduce MDAgents, the first adaptive decision-making framework for LLMs that mirrors real-world MDM processes via dynamic collaboration among AI agents based on task complexity. 2. MDAgents demonstrate superior performance in accuracy over previous solo and group methods on 7 out of 10 medical benchmarks, and we show an effective trade-off between performance and efficiency (i.e. the number of API calls) by varying the number of agents. 3. We provide rigorous testing under various hyperparameters (e.g. temperatures), demonstrating better robustness of MDAgents compared to solo and group methods. Furthermore, our ablations evidence MDAgents' ability to find the appropriate complexity level for each MDM instance. 
related works: 
 Language Models in Medical Decision-Making LLMs have shown promise in a range of applications within the medical field [14, 37, 40, 48, 63, 75, 76, 90, 96] . They can answer questions from medical exams [43, 52] , perform biomedical research [36] , clinical risk prediction [37] , and clinical diagnosis [55, 67] . Medical LLMs are also evaluated on generative tasks, including creating medical reports [79] , describing medical images [81] , constructing differentials [53] , performing diagnostic dialogue with patients [77] , and generating psychiatric evaluations of interviews [24] . To advance the capabilities of medical LLMs, two main approaches have been explored: (1) training with Table 1 : Comparison between our framework and previous methods (Solo and Group). Among these works, MDAgents is the only one to perform all key dimensions of LLM decision-making.Method MDAgents (Ours) Single Voting [82] Debate [17] MedAgents [72] ReConcile [10] Interaction TypeMultiple Roles✓ ✗ ✓ ✓ ✓ ✓ Early Stopping ✓ ✗ ✓ ✓ ✓ ✗ Refinement ✓ ✗ ✗ ✓ ✓ ✗ Complexity Check ✓ ✗ ✗ ✗ ✗ ✗ Multi-party Chat ✓ ✗ ✗ ✓ ✗ ✗ Conversation Pattern Flexible Static Static Static Static Staticdomain-specific data [28] , and (2) applying inference-time techniques such as prompt engineering [67] and Retrieval Augmented Generation (RAG) [92] . While initial research has been concentrated on pre-training and fine-tuning with medical knowledge, the rise of large general-purpose LLMs has enabled training-free methods where models leverage their latent medical knowledge. For example, GPT-4 [60] , with richer prompt crafting, surpasses the passing score on USMLE by over 20 points and with prompt tuning can outperform fine-tuned models including Med-PaLM [58, 59] . The promise of general-purpose models has thus inspired various techniques such as Medprompt and ensemble refinement to improve LLM reasoning [67] , as well as RAG tools that use external resources to improve the factuality and completeness of LLM responses [38, 92] . Frameworks like MEDIQ [49] and UoT [33] advance LLM reliability in clinical settings by enhancing information-seeking through adaptive question-asking and uncertainty reduction, supporting more realistic diagnostic processes. Our approach leverages these techniques and the capabilities of general-purpose models while acknowledging that a solitary LLM [37, 48, 90] may not fully encapsulate the collaborative and multidisciplinary nature of real-world MDM. We thus emphasize joinging multiple expert LLMs for effective collaboration in order to solve complicated medical tasks with greater accuracy. Multi-Agent Collaboration An array of studies have explored effective collaboration frameworks between multiple LLM agents [47, 86] to enhance capability above and beyond an individual LLM [80] . A common framework is role-playing, where each agent adopts a specific role (e.g. an Assistant Agent or a Manager Agent), a task is then broken down into sub-steps and solved collaboratively [47, 86] . While role-playing focuses on collaboration and multi-step problem-solving [88] , another framework, "multi-agent debate", prompts each agent to solve the task independently [17] . Then, they reason through other agents' answers to converge on a shared response, this approach can improve the factuality, mathematical ability and reasoning capabilities of the multi-agent solution [17, 50] . Similar frameworks include voting [82] , multi-disciplinary collaboration [72] , group discussions (ReConcile [10] ), and negotiating [23] . Table 1 compares existing setups across key dimensions in multi-agent interaction. Although these frameworks have shown improvement in the respective tasks, they rely on a pre-determined number of agents and interaction settings. When applied on a wider variety of tasks , this static architecture may lead to suboptimal multi-agent configurations, negatively impacting performance [51] . Furthermore, multi-agent approaches run the risk of being computationally inefficient or expensive to employ and need to justify these costs with noticable performance gains [17] . Given that different models and frameworks could generalize better to different tasks [93] , we propose a framework that dynamically assigns the optimal collaboration strategy at inference time based on the complexity of the query. We apply our strategy to MDM, a task that requires teamwork and should benefit from multi-agent collaboration [72] .Question: Does the paper discuss the limitations of the work performed by the authors?Answer: [Yes]Justification: The paper includes a section on limitations, discussing the scope of the framework, and areas for future improvement.Guidelines:• The answer NA means that the paper has no limitation while the answer No means that the paper has limitations, but those are not discussed in the paper. • The authors are encouraged to create a separate "Limitations" section in their paper.• The paper should point out any strong assumptions and how robust the results are to violations of these assumptions (e.g., independence assumptions, noiseless settings, model well-specification, asymptotic approximations only holding locally). The authors should reflect on how these assumptions might be violated in practice and what the implications would be. • The authors should reflect on the scope of the claims made, e.g., if the approach was only tested on a few datasets or with a few runs. In general, empirical results often depend on implicit assumptions, which should be articulated. • The authors should reflect on the factors that influence the performance of the approach.For example, a facial recognition algorithm may perform poorly when image resolution is low or images are taken in low lighting. Or a speech-to-text system might not be used reliably to provide closed captions for online lectures because it fails to handle technical jargon. • The authors should discuss the computational efficiency of the proposed algorithms and how they scale with dataset size. • If applicable, the authors should discuss possible limitations of their approach to address problems of privacy and fairness. • While the authors might fear that complete honesty about limitations might be used by reviewers as grounds for rejection, a worse outcome might be that reviewers discover limitations that aren't acknowledged in the paper. The authors should use their best judgment and recognize that individual actions in favor of transparency play an important role in developing norms that preserve the integrity of the community. Reviewers will be specifically instructed to not penalize honesty concerning limitations. 
mdagents: medical decision-making agents: 
 The design of MDAgents (Figures 1 and 2 ) incorporates four stages: 1) Medical Complexity Check -The system evaluates the medical query, categorizing it as low, moderate, or high complexity based on clinical decision-making techniques [6, 7, 21, 71, 84] . 2) Expert Recruitment -Based on complexity, the framework activates a single Primary Care Clinician (PCC) for low complexity issues, or a Multi-disciplinary Team (MDT) or Integrated Care Team (ICT) for moderate or high complexities [7, 18, 21, 34, 45, 71] . 3) Analysis and Synthesis -Solo queries use prompting techniques like Chain-of-Thought (CoT) and Self-Consistency (SC). MDTs involve multiple LLM agents forming a consensus, while ICTs synthesize information for the most complex cases. 4) Decision-making -The final stage synthesizes all inputs to provide a well-informed answer to the medical query.Question: What does the image of Gross Sectioned Myelolipoma demonstrate?Step 2. Expert Recruitment Moderator. The moderator agent functions as a general practitioner (GP) or emergency department doctor who first triages the medical query. This agent assesses the complexity of the problem and determines whether it should be handled by a single agent, a MDT, or an ICT. The moderator ensures the appropriate pathway be selected based on the query's complexity and oversees the entire decision-making process.Recruiter. The recruiter agent is responsible for assembling the appropriate team of specialist agents based on the complexity assessment of the moderator. The recruiter may assign a single PCP agent for low-complexity cases, while MDT or ICT with relevant expertise will be formed for moderate and high-complexity cases.General Doctor/Specialist. These agents are domain-specific or general physicians recruited by the recruiter agent. Depending on the complexity of the case, they may work independently or as part of a team. General physicians handle less complex, routine cases, whereas specialists are recruited for their specific expertise in more complex scenarios. These agents engage in the collaborative decision-making process, contributing their specialized knowledge to reach a consensus or provide detailed reports for high-complexity cases.The first step in the MDAgents framework is to determine the complexity of a given medical query q by the moderator LLM which functions as a generalist practitioner (GP). The moderator aims to act as a classifier to return the complexity level of the given medical query, it is provided with the information on how medical complexity should be defined and is instructed to classify the query into one of three different complexity levels:1. Low -Simple, well-defined medical issues that can be resolved by a single PCP agent. These typically include common, acute illnesses or stable chronic conditions where the medical needs are predictable and require minimal interdisciplinary coordination. 2. Moderate -The medical issues involve multiple interacting factors, necessitating a collaborative approach among an MDT. These scenarios require integration of diverse medical knowledge areas and coordination between specialists through consultation to develop effective care strategies. 3. High -Complex medical scenarios that demand extensive coordination and combined expertise from an ICT. These cases often involve multiple chronic conditions, complicated surgical or trauma cases, and decision-makings that integrates specialists from different healthcare departments. 3.3 Expert Recruitment (Line 3, 7, 17 of Algorithm 1) Given a medical query, the goal of the recruiter LLM is to enlist domain experts as individuals, in groups, or as multiple teams, based on the complexity levels determined by the moderator LLM. Specifically, we assign medical expertise and roles to multiple LLMs, instructing them to either act independently as solo medical agents or collaborate with other medical experts in a team. In Figure 9 in the Appendix, we also provide frequently recruited agents for each benchmark as a reference.The initial assessment protocol of our decision-making framework categorizes query complexity into low, moderate, and high. This categorization is grounded in established medical constructs such as acuity [25] for straightforward cases, comorbidity [69] and case management complexity [13] for intermediate and multi-disciplinary care requirements, and severity of illness [16] for high complexity cases requiring comprehensive management. We outlines the specific refinement approach: Low -Straightforward cases (Line 2-4 of Algorithm 1). For queries classified under Low complexity, characterized by straightforward clinical decision pathways, a single PCP agent (Figure 10 (a)) is deployed by the definition in Section 3.2. The domain expert who is recruited by recuriter LLM, applies few-shot prompting to the problem. The output answer, denoted as ans, is directly obtained from the agent's response to Q without the need for iterative refinement, formalized as ans = Agent(Q), with Agent representing the engaged PCP agent. Moderate -Intermediate complexity cases (Line 6-14 of Algorithm 1). In addressing more complex queries, the utilization of an MDT (Figure 10 (b) and (c)) approach has been increasingly recognized for its effectiveness in producing comprehensive and nuanced solutions [45] . The MDT framework leverages the collective expertise of professionals from diverse disciplines, facilitating a holistic examination of the query at hand. This collaborative method is particularly advantageous in scenarios where the complexity of a problem transcends the scope of a single domain, necessitating a fusion of insights from various specialties [7, 71] . The MDT approach not only enhances decision-making quality through the integration of multidimensional perspectives but also significantly improves the adaptability and efficiency of the problem-solving process [21] . Building upon this foundation, our framework specifically addresses queries of moderate complexity through a structured, multi-tiered collaborative approach. An MDT recruited by recruiter LLM (see Figure 10 in Appendix) starts an iterative discussion process aimed at reaching a consensus with at most R rounds (Line 10-12). For each round r ∈ R, agents A i , i ∈ 1, . . . , N indicate participation and preferred interlocutors. The system facilitates message exchanges for T turns. If consensus is not reached and agents agree to continue, a new round begins with access to previous conversations. For every round, consensus within the MDT is determined by parsing and comparing their opinions. In the event of a disagreement, the moderator agent, consistent with the one described in Section 3.2 reviews the MDT's discourse and formulates feedback for each agent. High -Complex care cases (Line 17-24 of Algorithm 1). In contrast to the MDT approach, the ICT (Figure 10 (d) ) paradigm is essential for addressing the highest tier of query complexity in healthcare. This structured progression through the ICT ensures a depth of analysis that is specialized and focused at each stage of the decision-making process. Beginning with the Initial Assessment Team, moving through various diagnostic teams, and culminating with the Final Review & Decision Team, our ICT model aligns specialist insights into a cohesive narrative that informs the ultimate decision (Appendix Algorithms 1 Lines 19-21). A key component of this process is the report generation process described in Appendix with the prompt, where each team, led by a lead clinician, collaboratively produces a comprehensive report synthesizing their findings. This phased approach, supported by evidence from recent healthcare studies, has been shown to enhance the precision of clinical decision-making, as each team builds upon the foundation laid by the previous, ensuring a meticulous and refined examination of complex medical cases [34] . The resultant reports, accumulating throughout the ICT process, are not only reflective of comprehensive medical evaluations but also of a systematic and layered analysis that is critical in the management of intricate health scenarios [18] .In the final stage of our framework, the decision-maker agent synthesizes the diverse inputs generated throughout the decision-making process to arrive at a well-informed final answer to the medical query q. This synthesis involves several components depending on the complexity level of the query: 1. Low: Directly utilizes the initial response from the primary decision-making agent. 2. Moderate: Incorporates the conversation history (Interaction) between the recruited agents to understand the nuances and disagreements in their responses. 3. High: Considers detailed reports (Reports) generated by the agents, which include comprehensive analyses and justifications for their diagnostic suggestions. The decision-making process is formulated as ans = Agent(•) where the final answer, ans is determined by integrating the outputs from analysis and synthesis step based on its medical complexities. This integration employs ensemble techniques such as temperature ensembles to ensure the decision is robust and reflects a consensus among the models when applicable (see Appendix C.2 for details).  
experiments and results: 
 In this section, we evaluate our framework and baseline methods across different medical benchmarks in Solo, Group, and Aaptive settings. Our experiments and ablation studies highlight the framework's performance, demonstrating robustness and efficiency by modulating agent numbers and temperatures.Results also show a beneficial convergence of agent opinions in collaborative settings.To verify the effectiveness of our framework, we conduct comprehensive experiments with baseline methods on ten datasets including MedQA, PubMedQA, DDXPlus, SymCat, JAMA, MedBullets, Path-VQA, PMC-VQA, MIMIC-CXR and MedVidQA. A detailed explanation and statistics for each dataset are deferred to Appendix A and Figure 8 . We use 50 samples per dataset for testing, and the inference time for each complexity was: low -14.7s, moderate -95.5s, and high -226s in average. For all the quantitative experiments in this section, we compare three settings: (1) Solo: Using a single LLM agent in the decision-making state. (2) Group: Implementing multi-agents to collaborate during the decision-making process. (3) Adaptive: Our proposed method MDAgents, adaptively constructs the inference structure from PCP to MDT and ICT. We use 3-shot prompting for low-complexity cases and zero-shot prompting for moderate and high-complexity cases across all settings. Medical Question Answering With MedQA [35] , PubMedQA [36] , MedBullets [9] , and JAMA [9] , we focus on question answering through text, involving both literature-based and conceptual medical knowledge questions. Specifically, PubMedQA tasks models to answer questions using abstracts from PubMed, requiring synthesis of biomedical information. MedQA tests the model's ability to understand and respond to multiple-choice questions derived from medical educational materials and examinations. MedBullets provides USMLE Step 2/3 type questions that demand the application of medical knowledge and clinical reasoning. JAMA Clinical Challenge presents challenging real-world clinical cases with diagnosis or treatment decision-making questions, testing the model's clinical reasoning (Figure 8 in Appendix shows complexity distribution for each dataset) Diagnostic Reasoning DDXPlus [73] and SymCat [2] involve clinical vignettes that require differential diagnosis, closely mimicking the diagnostic process of physicians. These tasks test the model's ability to reason through symptoms and clinical data to suggest possible medical conditions, evaluating the AI's diagnostic reasoning abilities similar to a clinical setting. SymCat [2] uses synthetic patient records constructed from a public disease-symptom data source and is enhanced with additional contextual information through the NLICE method. Medical Visual Interpretation Path-VQA [30] , PMC-VQA [95] , MedVidQA [29] , and MIMIC-CXR [3] challenge models to interpret medical images and videos, requiring integration of visual data with clinical knowledge. PathVQA focuses on answering questions based on pathology images, testing AI's capability to interpret complex visual information from medical images. PMC-VQA evaluates AI's proficiency in deriving answers from both text and images found in scientific publications. MedVidQA extends to video-based content, where AI models need to process information from medical procedure videos. MIMIC-CXR-VQA specifically targets chest radiographs, utilizing a diverse and large-scale dataset designed for visual question-answering tasks in the medical domain.Baseline Methods • Solo: The baseline methods considered for the Solo setting include the following: Zero-shot [41] directly incorporates a prompt to facilitate inference, while Few-shot [8] involves a small number of examples. Few-shot CoT [83] integrates rationales before deducing the answer. Few-shot CoT-SC [82] builds upon Few-shot CoT by sampling multiple chains to yield the majority answer. Ensemble Refinement (ER) [67] is a prompting strategy that conditions model responses on multiple reasoning paths to bolster the reasoning capabilities of LLMs. Medprompt [59] is a composition of several prompting strategies that enhances the performance of LLMs and achieves state-of-the-art results on multiple benchmark datasets, including medical and non-medical domains. • Group: We tested five group decision-making methods: Voting [82] , MedAgents [72] , Reconcile [10] , AutoGen [86] , and DyLAN [51] . Autogen was based on four agents, with one User, one Clinician, one Medical Expert, and one Moderator, with one response per agent [86] . DyLAN setup followed the base implementations of four agents with no specific roles and four maximum rounds of interaction [51] . While the methods support multiple models, GPT-4 was used for all agents.In Table 2 , we report the classification accuracy on MedQA, PubMedQA, DDXPlus, SymCat, JAMA, MedBullets, Path-VQA, PMC-VQA and MedVidQA dataset. We compare our method (Adaptive) with several baselines in both Solo and Group settings. Adaptive method outperforms Solo and Group settings. As depicted in Figure 4 and Table 2 , MDAgents significantly outperforms (p < 0.05) both Solo and Group setting methods, showing best performance in 7 out of 10 medical benchmarks tested. This reveals the effectiveness of adaptive strategies integrated within our system, particularly when navigating through the text-only (e.g., DDXPlus where it outperformed the best performance of single-agent by 5.2% and multi-agent by 9.5%) and text-image datasets (e.g., Path-VQA, PMC-VQA and MIMIC-CXR). Our approach not only comprehends textual information with high precision but also adeptly synthesizes visual data, a pivotal capability in medical diagnostic evaluations. Why Do Adaptive Decision-making Framework Work Well? It is important to accurately assign difficulty levels to medical questions. For instance, if a medical question is obviously easy, utilizing a team of specialists (such as an IDT) might be excessive and potentially lead to overly pessimistic approaches. Conversely, if a difficult medical question is only tackled by a PCP, the problem might not be adequately addressed. The core issue here is the LLM's capability to classify the difficulty of medical questions appropriately. If an LLM inaccurately classifies the difficulty level, the chosen medical solution may not be suitable, potentially leading to the wrong decision making. Therefore, understanding what constitutes an appropriate difficulty level is essential.We hypothesize that an LLM, functioning as a classifier, will select the optimal complexity level for each MDM problem. This hypothesis is supported by Figure 3 , which illustrates that the model appropriately matches the complexity levels; low, moderate, and high of the given problem. To determine this, we assessed the accuracy of solutions across various difficulty levels. Specifically, we evaluated 25 medical problems by repeating each problem for 10 times at each difficulty level. By measuring the success rate, we aimed to identify the difficulty level that yielded the highest accuracy. This approach ensures that the LLM's complexity classification aligns with the most effective and accurate medical solutions, thereby optimizing the application of medical expertise to each question. Formally, for any given problem P , we denote the probability that the correct answer can be solved at a specific complexity level as p complexity-level (P ), where complexity-level ∈ {low, moderate, high}. arg max(P ) ∈ {low, moderate, high} refers to the complexity level that has the highest probability among p low (P ), p moderate (P ), and p high (P ). Similarly, arg min(P ) is the complexity level with the lowest probability, and arg mid(P ) is the one with the middle probability. We denote a, b, and c as the probabilities that the LLM selects the complexity levels corresponding to arg max, arg mid, and arg min, respectively. Thus, the accuracy of our system for problem P can be described by a , highlighting the advantage of collaborative process in complex tasks. This result aligns with findings from [4] , which showed that pooled diagnoses from groups of physicians significantly outperformed those of individual physicians, with accuracy increasing as group size increased. Overall, solo settings outperformed group settings in four benchmarks, while group settings outperformed solo in six benchmarks. These results reveals that while solo methods excel in straightforward tasks, group settings provide better performance in complex, multi-faceted tasks requiring diverse expertise.Impact of Complexity Selection. We evaluate the importance of the complexity assessment and adaptive process through ablation studies (Figure 5 ). Our adaptive method significantly outperforms static complexity assignments across different modality benchmarks. For text-only queries, the Adaptive method achieves an accuracy of 81.2%, significantly higher than low (64.2%), moderate (71.6%), and high (65.8%) settings. Interestingly, 64% of the text-only queries were classified as high complexity, indicating that many text-based queries required in-depth analysis with different expertise. In the image + text modality, the Adaptive method classified 55% of the queries as low complexity, suggesting that the visual information often provides clear and straightforward cues that simplify the decision-making process. Finally, for video + text queries, 87% of these queries were classified as low complexity, reflecting that the dynamic visual data in conjunction with text can often be straightforwardly interpreted. However, further evaluation on more challenging video medical datasets is needed, as MedVidQA contains relatively less complex medical knowledge. Impact of Moderator's Review and RAG Table 3 examines the impact of incorporating external medical knowledge and moderator reviews into the MDAgents framework on accuracy. MedRAG [89] is a systematic toolkit for Retrieval-Augmented Generation (RAG) that leverages various corpora; biomedical, clinical and general medicine, to provide comprehensive knowledge. The baseline accuracy of MDAgents is 71.8%. Integrating MedRAG increases accuracy to 75.2% (up 4.7%), while the moderator's review alone raises it to 77.6% (up 8.1%). The combined use of both methods achieves the highest accuracy at 80.3% (up 11.8%). The results indicate that MedRAG and moderator review both enhance performance, with their combined effect being synergistic. This highlights that leveraging recent external knowledge and structured feedback mechanisms is crucial for refining and converging on accurate medical decisions. This improvement underscores the importance of a hybrid strategy, aligning with real-world practices of continuous learning and expert consultation to optimize performance in medical applications.Our experiment with varying the number of agents in a collaborative Group setting (Appendix Figure 6 (a-b )) shows that a higher number of agents does not lead to better performance. Rather, our Adaptive method achieves optimal performance with fewer agents (N=3, peak accuracy of 83.5%) by intelligently calibrating the number of collaborating agents. This not only indicates efficiency in decision-making but also computational and economic benefits, considering the reduced number of API calls needed, especially when contrasted with the Solo and Group settings. With regards to computational efficiency, the Solo setting (5-shot CoT-SC) resulted in a 6.0 and Group setting (MedAgents with N=5) resulted in a 20.3 API calls, suggesting a high computational cost without a corresponding increase in accuracy. On the other hand, our Adaptive method exhibits a more economical use of resources, demonstrated by fewer API calls (9.3 with N=3) while maintaining high accuracy, a critical factor in deploying scalable and cost-effective medical AI solutions.Our Adaptive approach shows resilience to changes in temperature (Appendix Figure 6 (c), low (T=0.3) and high (T=1.2)) with performance improving under higher temperatures. This suggests that our model can utilize the creative and diverse outputs generated at higher temperatures to enhance decision-making, a property that is not as pronounced in the Solo and Group conditions. This robustness is particularly valuable in real-world medical domains with high uncertainty and ambiguity in datasets [15] . Additionally, studies have shown that creative diagnostic approaches can mitigate cognitive biases and improve diagnostic accuracy [66] , while fostering flexibility and adaptability in decision-making [19] . These insights support the enhanced performance observed under higher temperatures in our framework. However, the future work should explore a wider range of temperatures to fully understand the robustness and adaptability of our approach.There is clear trend towards consensus among MDAgents cross various data modalities (Figure 7 ). The text+video modality demonstrates a rapid convergence, reflecting the agents' efficient processing of combined textual and visual cues. On the other hand, the text+image and text-only modalities display a more gradual decline in entropy, indicating a progressive narrowing of interpretative diversity among the agents. Despite the differing rates and initial conditions, all modalities exhibit convergence of agent opinions over time. This uniformity in reaching consensus highlights the MDAgents' capability to integrate and reconcile information. Please refer to Appendix B for a detailed explanation of the entropy calculation. 
conclusion: 
 This paper introduces MDAgents, a framework designed to enhance the utility of LLMs in complex medical decision-making by dynamically structuring effective collaboration models. To reflect the nuanced consultation aspects in clinical settings, MDAgents adaptively assigns LLMs either to roles independently or within groups, depending on the task's complexity. This emulation of real-world medical decision processes has been comprehensively evaluated, with MDAgents outperforming previous solo and group methods in 7 out of 10 medical benchmarks. The case study illustrates the practical efficacy and collaborative dynamics of our proposed framework, providing insights into how differing expert opinions are synthesized to reach a more accurate diagnosis. This is evidenced by our agents' ability to converge on the correct diagnosis despite initially divergent perspectives. Ablation studies further elucidate the individual contributions of agents and strategies within the system, revealing the critical components and interactions that drive the framework's success. By harnessing the strength of multi-modal reasoning and fostering a collaborative process among LLM agents, our framework opens up new possibilities for enhancing LLM-assisted medical diagnosis systems, pushing the boundaries of automated clinical reasoning.The decision is challenging due to the conflicting interpretations of the image. However, the I believe that the image of Gross Sectioned Myelolipoma demonstrates (C) hemorrhage (red arrow). Hemorrhage is the escape of blood from a blood vessel.I understand that you believe the image shows (A) fibrosis, but I disagree. I believe that the image shows hemorrhage because the area pointed to by the red arrow is dark red and appears to be surrounded by a thin rim of white tissue. This is consistent with the appearance of hemorrhage, not fibrosis.I am also aware that you are a specialist in the study and diagnosis of disease through examination of tissue samples, but I believe that my expertise in oncology gives me a unique perspective on this image.I believe that my argument is strong and that I can convince the pathologist of my opinion. I am confident in my diagnosis of hemorrhage, and I am willing to discuss the image further to support my opinion. case studies reveals how our framework provides an environment for agents to collaborate, gather information, moderate and make final decisions in complex medical scenarios.Question: Based on the following information, please indicate the most likely diagnosis:Step 2. Expert Recruitment For further investigations:1. Ophthalmologist: Since double vision and eyelid drooping are some of the common symptoms of Myasthenia Gravis, the ophthalmologist may conduct a comprehensive eye examination. This might include testing for alterations in eye movement and eyelid strength. The findings from these investigations will not only support the Myasthenia Gravis diagnosis, they will also provide foundational data for the creation of a targeted rehabilitation plan. The plan will aim to maintain oral intake while ensuring safety, preventing any potential complications like aspiration pneumonia. Question: Does the paper provide open access to the data and code, with sufficient instructions to faithfully reproduce the main experimental results, as described in supplemental material?Answer: [Yes]Justification: The paper includes a link to the code repository and provides sufficient instructions in the supplemental material to reproduce the experiments.Guidelines:• The answer NA means that paper does not include experiments requiring code. • Providing as much information as possible in supplemental material (appended to the paper) is recommended, but including URLs to data and code is permitted. 
experimental setting/details: 
 Question: Does the paper specify all the training and test details (e.g., data splits, hyperparameters, how they were chosen, type of optimizer, etc.) necessary to understand the results?Answer: [Yes]Justification: The paper specifies all relevant experimental details, including data splits, number of samples, and number of seeds, ensuring transparency and reproducibility of the results.Guidelines:• The answer NA means that the paper does not include experiments.• The experimental setting should be presented in the core of the paper to a level of detail that is necessary to appreciate the results and make sense of them. • The full details can be provided either with the code, in appendix, or as supplemental material. 
experiment statistical significance: 
 Question: Does the paper report error bars suitably and correctly defined or other appropriate information about the statistical significance of the experiments?Answer: [Yes]Justification: The paper reports error bars and includes information on the statistical significance of the experimental results, ensuring a clear understanding of the variability and reliability of the findings.Guidelines:• The answer NA means that the paper does not include experiments.• The authors should answer "Yes" if the results are accompanied by error bars, confidence intervals, or statistical significance tests, at least for the experiments that support the main claims of the paper. • The factors of variability that the error bars are capturing should be clearly stated (for example, train/test split, initialization, random drawing of some parameter, or overall run with given experimental conditions). • The method for calculating the error bars should be explained (closed form formula, call to a library function, bootstrap, etc.) • The assumptions made should be given (e.g., Normally distributed errors). • It should be clear whether the error bar is the standard deviation or the standard error of the mean. • It is OK to report 1-sigma error bars, but one should state it. The authors should preferably report a 2-sigma error bar than state that they have a 96% CI, if the hypothesis of Normality of errors is not verified. • For asymmetric distributions, the authors should be careful not to show in tables or figures symmetric error bars that would yield results that are out of range (e.g. negative error rates). • If error bars are reported in tables or plots, The authors should explain in the text how they were calculated and reference the corresponding figures or tables in the text. 
experiments compute resources: 
 Question: For each experiment, does the paper provide sufficient information on the computer resources (type of compute workers, memory, time of execution) needed to reproduce the experiments? Answer: [Yes] Justification: The experiments primarily involved inference using API calls to GPT-3.5, GPT-4 (V), and Gemini-Pro (Vision). The type of compute workers, memory, and time of execution are managed by the API providers (OpenAI and Gemini). Details about the number of API calls and the specific configurations used for each experiment are provided, ensuring reproducibility. Guidelines:• The answer NA means that the paper does not include experiments.• The paper should indicate the type of compute workers CPU or GPU, internal cluster, or cloud provider, including relevant memory and storage. • The paper should provide the amount of compute required for each of the individual experimental runs as well as estimate the total compute. • The paper should disclose whether the full research project required more compute than the experiments reported in the paper (e.g., preliminary or failed experiments that didn't make it into the paper). • The authors should make sure to preserve anonymity (e.g., if there is a special consideration due to laws or regulations in their jurisdiction). 10. Broader Impacts Question: Does the paper discuss both potential positive societal impacts and negative societal impacts of the work performed?Answer: [Yes] Justification: The paper discusses potential societal impacts, both positive and negative, including the improvement of medical decision-making and the risks associated with misuse or biases in the AI system. Guidelines:• The answer NA means that there is no societal impact of the work performed.• If the authors answer NA or No, they should explain why their work has no societal impact or why the paper does not address societal impact. • Examples of negative societal impacts include potential malicious or unintended uses (e.g., disinformation, generating fake profiles, surveillance), fairness considerations (e.g., deployment of technologies that could make decisions that unfairly impact specific groups), privacy considerations, and security considerations. • The conference expects that many papers will be foundational research and not tied to particular applications, let alone deployments. However, if there is a direct path to any negative applications, the authors should point it out. For example, it is legitimate to point out that an improvement in the quality of generative models could be used to generate deepfakes for disinformation. On the other hand, it is not needed to point out that a generic algorithm for optimizing neural networks could enable people to train models that generate Deepfakes faster. • The authors should consider possible harms that could arise when the technology is being used as intended and functioning correctly, harms that could arise when the technology is being used as intended but gives incorrect results, and harms following from (intentional or unintentional) misuse of the technology. • If there are negative societal impacts, the authors could also discuss possible mitigation strategies (e.g., gated release of models, providing defenses in addition to attacks, mechanisms for monitoring misuse, mechanisms to monitor how a system learns from feedback over time, improving the efficiency and accessibility of ML). 
safeguards: 
 Question: Does the paper describe safeguards that have been put in place for responsible release of data or models that have a high risk for misuse (e.g., pretrained language models, image generators, or scraped datasets)? Answer: [Yes] Justification: The paper discusses potential limitations, including the risks of medical hallucinations and false knowledge. It includes guidelines within the main text to ensure responsible use of the MDAgents framework and publicly available datasets, emphasizing ethical usage and adherence to best practices. Guidelines:• The answer NA means that the paper poses no such risks.• Released models that have a high risk for misuse or dual-use should be released with necessary safeguards to allow for controlled use of the model, for example by requiring that users adhere to usage guidelines or restrictions to access the model or implementing safety filters. • Datasets that have been scraped from the Internet could pose safety risks. The authors should describe how they avoided releasing unsafe images. • We recognize that providing effective safeguards is challenging, and many papers do not require this, but we encourage authors to take this into account and make a best faith effort. 12. Licenses for existing assets Question: Are the creators or original owners of assets (e.g., code, data, models), used in the paper, properly credited and are the license and terms of use explicitly mentioned and properly respected? Answer: [Yes] Justification: The paper properly credits the creators of existing assets used and clearly states the licenses and terms of use. 
 
 Appendix: Despite the successes of our framework in showing promising performance in medical decisionmaking tasks, we recognize several limitations that open pathways for future research.Medical Focused Foundation Models. An essential enhancement would be to incorporate the foundation models and systems specifically trained on medical data, like Med-Gemini [63] , AMIE [77] , and Med-PaLM 2 [67] . These models excel in generating professional medical terminologies, which can facilitate more effective and accurate communication between multiple agents involved in the decision-making process. By leveraging these specialized models, the agents can interact using a shared, precise medical vocabulary, enhancing the system's overall performance and reliability. This approach not only ensures more medically accurate content generation but also supports better collaboration and understanding among the agents, which is essential for complex medical decisionmaking tasks.Patient-Centered Diagnosis. A primary limitation lies in the fact that our current framework operates within the confines of multiple-choice question answering and does not account for the interactive, patient-centered nature of real-world diagnostics. Effective diagnosis often relies on continuous exchanges that include the patient's narrative, the physician's expertise, and input from caregivers. To bridge this gap, future iterations of our framework will aim to incorporate a more interactive system that not only assists physicians but also directly engages with both patients and caregivers in a multi-stakeholder approach. Moreover, by incorporating regret-aware [91] decisionmaking, the system can learn to minimize diagnostic regret over time, refining its responses based on the outcomes of prior interactions. This regret-aware framework will help guide the LLM to seek additional information when uncertainties arise, thereby supporting more informed decisions across complex, multi-stakeholder scenarios. Embedding these real-world interactions within the feedback loop will enable the system to provide more nuanced and patient-centric support, enhancing the quality and personalization of medical decision-making across all involved parties.Potential Risks and Mitigations. While our framework shows promise, potential risks include medical hallucinations and the generation of inaccurate or misleading information. To address these risks, integrating self-correction mechanisms, such as those proposed by [42] , could enable the model to autonomously identify and rectify its own errors via reinforcement learning-based self-correction. Additionally, implementing rule-based reward structures, as suggested in [56] , would allow the model to adhere to specific safety and accuracy guidelines during training. These methods can support a safer, more reliable diagnostic support tool by introducing corrective feedback loops and standardized behavior guidelines. Furthermore, integrating confidence scores and uncertainty estimates with the model's recommendations could enhance the decision-making process by enabling end-users to weigh various diagnostic options, ultimately increasing the system's trustworthiness and safety.We evaluate multi-agent collaboration frameworks across seven common medical question-answering datasets, which vary in question complexity. Generally, questions are deemed more complex if they involve multiple modalities or entail a lengthy, detailed diagnostic task. Below, we detail each dataset and provide a sample entry: [36] . It requires yes/no/maybe answers to questions grounded in PubMed abstract. The dataset comprises entries each containing a question, a relevant abstract minus the conclusion, and a ground truth label. We used 50 samples for testing. Given its binary choice format, we consider the complexity of this dataset to be low. Sample Question: "Can predilatation in transcatheter aortic valve implantation be omitted?" Context: "The use of a balloon expandable stent valve includes balloon predilatation of the aortic stenosis before valve deployment. The aim of the study was to see whether or not balloon predilatation is necessary in transcatheter aortic valve replacement (TAVI). Sixty consecutive TAVI patients were randomized to the standard procedure or to a protocol where balloon predilatation was omitted. There were no significant differences between the groups regarding early hemodynamic results or complication rates."DDXPlus. DDXPlus is a medical diagnosis dataset using synthetic patient information and symptoms [73] . Each instance represents a patient, with attributes including age, sex, initial evidences, evidence, multiple options of possible pathologies, and a ground truth diagnosis. Due to its text-only and multiple-choice nature, we consider the complexity to be low. Sample Patient Information: Age: 96, Sex: F Evidences: ['e66', 'insp_siffla', 'j45', 'posttus_emesis', 'trav1_@_N', 'vaccination'] Initial evidence: 'posttus_emesis' Options: (A) Bronchite (B) CoquelucheMedVidQA. MedVidQA dataset consists of 3,010 health-related questions with visual answers from validated video sources (e.g. medical school, health institutions, etc). We enhanced the dataset by using GPT-4 to generate multiple-choice answers, including one correct 'golden answer' and several false options, expanding its use for training and evaluating automated medical question-answering systems. Sample Question: How to perform corner stretches to treat neck pain? Video: h5MvX50zTLM.mp4 Options: A: By bending your knees and touching your toes, B: By performing jumping jacks, C: By leaning into a corner with your elbows up at shoulder level, D: By doing push-ups 7. SymCat. SymCat is a synthetic dataset which includes 5 million symptom-condition samples, covering 801 distinct conditions each with 376 potential symptoms dataset [2] . Sample Patient Information: Age: 42, Gender: F, Race: White, Ethnicity: Nonhispanic Sample Patient Symptoms: Pain:cramping:Abdomen:Lower abdomen::::Worsening after meals::32; Altered stool:Lumpy:::::::Often:44; Flatulence:::::::::30; Bloating:::::::::41 Options: "A": "Asthma", "B": "IBS (Constipation type)", "C": "Viral meningitis (Varicella zoster virus)", "D": "Bacterial (Gastro)enteritis (Yersinia infection most likely)"8. JAMA. JAMA includes 1524 clinical cases collected from the JAMA Network Clinical Challenge archive, which are summaries of actual challenging clinical cases. Each sample is framed as a question, with a long case description and four options [9] . Sample Case: A 62-year-old woman undergoing peritoneal dialysis (PD) for kidney failure due to IgA nephropathy presented to the PD clinic with a 1-day history of severe abdominal pain and cloudy PD fluid. Seven days prior, she inadvertently broke aseptic technique when tightening a leaking connection of her PD catheter tubing. On presentation, she was afebrile and had normal vital signs. Physical examination revealed diffuse abdominal tenderness. Cloudy fluid that was drained from her PD catheter was sent for laboratory analysis (Table 1 ).Await peritoneal dialysis fluid culture results before starting intraperitoneal antibiotics. Sample Question: What Would You Do Next? "A": "Administer empirical broad-spectrum intraperitoneal antibiotics", "B": "Administer empirical broad-spectrum intravenous antibiotics", "C": "Await peritoneal dialysis fluid culture results before starting intraperitoneal antibiotics", "D": "Send blood cultures" Options: "A": "Botulinum toxin injections", "B": "Reassurance and reassessment in 1 month", "C": "Serial casting", "D": "Surgical pinning",The entropy H serves as an indicator of consensus progression among the agents. It is quantified as:H = - M i=1 p(x i ) log 2 p(x i ) (B.1)where M is the total number of unique answers, x i represents a unique answer, and p(x i ) is the probability of occurring among all answers. This calculation helps to measure the degree of agreement among the agents over time, with lower entropy indicating higher consensus. This section presents additional experimental results and analyses that provide further insights into the performance and characteristics of our MDAgents framework.You are a {{role}} who {{description}}. Your job is to collaborate with other medical experts in a team.Given the opinions from other medical agents in your team, please indicate whether you want to talk to any expert (yes/no). If not, provide your opinion. {{opinions}}Remind your medical expertise and leave your opinion to an expert you chose. Deliver your opinion once you are confident enough and in a way to convince other expert with a short reason.You are a final medical decision maker who reviews all opinions from different medical experts and makes the final decision.Given the {{inputs}}, please review the {{inputs}} and make the final answer to the question by {{decision_methods}} (e.g., majority voting, ensemble refinement). **Answer:**(Given the {{agent answers}}, please complete the following steps: 1. Take careful and comprehensive consideration of the provided reports.2. Extract key knowledge from the reports.3. Derive a comprehensive and summarized analysis based on the extracted knowledge. 4. Generate a refined and synthesized report based on your analysis.To provide a comprehensive evaluation of our approach, we conducted experiments on the entire MedQA 5-options dataset using GPT-4o mini. This expands upon the subsampled experiments presented in the main experiments in Table 2 . Below Table shows the accuracy results for various methods. These results demonstrate that our MDAgents approach outperforms both single-agent and other multi-agent methods across the full dataset, achieving an accuracy of 83.6%. This underscores the effectiveness of our framework in handling diverse medical questions at scale.To provide transparency and aid in reproducibility, we estimated the costs associated with running experiments on the entire test sets using GPT-4 (Vision). Below Table presents these cost estimates in USD for various datasets and methods. While our approach incurs higher costs due to its multi-agent nature, the significant performance improvements justify this increased computational expense for critical medical decision-making tasks. We investigated whether simply assigning roles to agents is sufficient for expert-like performance, and explored the impact of equipping agents with different knowledge using Retrieval-Augmented Generation (RAG). Table 7 presents the results of these experiments.These results indicate that while role assignment provides a foundation, augmenting agents with specific knowledge (using MedRAG) and structured reviews (Moderator's Review) significantly enhances their ability to simulate domain expertise. The combination of Moderator's Review and MedRAG yielded the best performance, highlighting the synergy between structured collaboration and domain-specific knowledge retrieval.To address the impact of complexity assignment on accuracy and API costs, we conducted additional experiments focusing on high-complexity cases, particularly in image+text scenarios. Table 8 shows the results for various collaborative settings. 8 : Impact of collaboration settings on high-complexity image+text tasks These results underscore the importance of multi-turn discussions, particularly in complex cases. The parallel collaboration with discussion yielded the highest accuracy (59.0%), suggesting that enabling agents to work simultaneously and engage in dialogue is crucial for handling intricate medical queries. The significant performance gap between discussion and no-discussion scenarios (45.0% vs. 39.0% for sequential, and 59.0% vs. 56.0% for parallel) highlights the value of interactive deliberation in medical decision-making processes. Majority Voting 80.6 ± 2.9 72.2 ± 6.9 56.9 ICT ← RECRUIT(Q, Complexity) ▷ Recruit an Integrated Care Team 25:Reports ← [] 26:for T eam ∈ ICT do 27:Report ← GENERATEREPORT(Q, T eam) ▷ Each Team curates a report 28:Reports ← Reports + [Report] 29:end for 30:ans ← Agent(Q, Reports) ▷ Final decision made 31: end if 32: return ansMDM requires efforts of both individual expertise and collaboration to navigate the complexities of patient care. Clinicians often face challenging scenarios that necessitate a comprehensive approach, integrating insights from various specialties to arrive at the best possible outcomes.Below are the real-world example cases that could be classified as low, moderate, to high complexity cases.Case 1: Adjusting Medication Dosage for Chronic Disease (Low Complexity) A 55-year-old female patient with type 2 diabetes visits her PCP for a routine check-up. The patient has been taking 500 mg of metformin orally twice a day and has been adhering to a low-carbohydrate diet. Upon testing with fasting glucose level, the glucose level is above normal. PCP reviewed the current medical dosage and increased the dosage to manage the blood glucose level of the patient.Case 2: Differential Diagnosis in the Emergency Department (Moderate Complexity) A 40year-old male patient arrives at the emergency department (ED) with a high fever, severe headache, and muscle pain, raising concerns about a potential infectious disease. The ED physician conducts an initial examination but recognizes the need for a more detailed evaluation to identify the underlying cause. The patient is referred to the infectious disease department for further assessment. An infectious disease specialist, along with the ED physician, reviews the patient's symptoms, travel history, and recent exposures. They collaborate on ordering specific diagnostic tests, including blood cultures and imaging studies. Through this teamed decision-making process, they diagnose the patient with dengue fever and promptly initiate appropriate antiviral treatment.A 60-year-old female patient with chronic heart failure has been experiencing new symptoms of shortness of breath and mild fever, suggesting either a complication due to her chronic heart failure or a new infection. The urgent care doctor identifies the severity of the situation and promptly refers the patient to the emergency department of a large hospital, where the patient has triaged to see a cardiologist and an infectious disease doctor for specialized care. The team conducts a detailed review of the patient's medication history and current symptoms, does a physical exam to listen to lung sounds, and orders a few exams including labs, a chest x-ray, echocardiogram, and electrocardiogram. The team identifies that the patient has pulmonary effusion and upper respiratory viral infection.Now, let us look at the cases from the MedQA [35] dataset that illustrate either individual PCP or teamed decision-making is crucial in managing medical conditions, ranging from low to high complexity levels of potential cases. These examples highlight the importance of checking the complexity of the case for proper management.Case 4: Diagnosis by PCP The case below with the "Low Complexity" header is classified as low complexity by a medical doctor. In this case, a PCP can answer this question without consulting a gastroenterologist. The diagnosis of gastric cancer and management based on the manifestation of the disease, that has been described in this question and beyond should be from a gastroenterologist. However, PCPs are expected to have the basic scientific and pathophysiological knowledge that is related to gastric cancer and use that knowledge to solve this problem.Case 5: Diagnosis and management by single Pediatric Endocrinologist The case below with the "Moderate Complexity" header is classified as moderate complexity by the medical doctor. In this case, a pediatric endocrinologist (specialist) alone can diagnose a patient and have a treatment plan. Note that this patient could have been referred to this pediatric endocrinologist by a PCP who is regularly seeing this patient.Question: A 70-year-old man comes to the physician because of a 4-month history of epigastric pain, nausea, and weakness. He has smoked one pack of cigarettes daily for 50 years and drinks one alcoholic beverage daily. He appears emaciated. He is 175 cm (5 ft 9 in) tall and weighs 47 kg (103 lb); BMI is 15 kg/m². He is diagnosed with gastric cancer. Which of the following cytokines is the most likely direct cause of this patient's examination findings? Answer:A) TGF-β B) IL-6 C) IL-2 D) TNF-βQuestion: A 5-year-old girl is brought to the clinic by her mother for excessive hair growth. Her mother reports that for the past 2 months she has noticed hair at the axillary and pubic areas. She denies any family history of precocious puberty and reports that her daughter has been relatively healthy with an uncomplicated birth history. She denies any recent illnesses, weight change, fever, vaginal bleeding, pain, or medication use. Physical examination demonstrates Tanner stage 4 development. A pelvic ultrasound shows an ovarian mass. Laboratory studies demonstrate an elevated level of estrogen. What is the most likely diagnosis? Answer:A) Granulosa cell tumor B) Idiopathic precocious puberty C) McCune-Albright syndrome D) Sertoli-Leydig tumorCase 6: Diagnosis and management by multidisciplinary team The case below is of a "High Complexity" patient primarily having neurological symptoms but with problems with vision, which requires a neurologist to consult to ophthalmology department for further evaluation.Question: A 63-year-old woman presents to her primary-care doctor for a 2-month history of vision changes, specifically citing the gradual onset of double vision. Her double vision is present all the time and does not get better or worse throughout the day. She has also noticed that she has a hard time keeping her right eye open, and her right eyelid looks 'droopy' in the mirror. Physical exam findings during primary gaze are shown in the photo. Her right pupil is 6 mm and poorly reactive to light. The rest of her neurologic exam is unremarkable. Laboratory studies show an Hb A1c of 5.0%. Which of the following is the next best test for this patient? Answer: A) Direct fundoscopy B) Intraocular pressures C) MR angiography of the head D) Temporal artery biopsy High ComplexityIn this section, we provide two examples of our framework with moderate (Figure 11 ) and high (Figure 12 ) complexity in PMC-VQA (image+text) and DDXPlus (text-only) respectively. These Step 2. Expert Recruitment I believe that the image shows (C) hemorrhage (red arrow). Hemorrhage is the escape of blood from a blood vessel. It can occur due to a variety of factors, including trauma, bleeding disorders, and certain medical conditions. In the image, the red arrow points to an area of the tissue that is dark red and appears to be surrounded by a thin rim of white tissue. This is consistent with the appearance of hemorrhage. In the image, the red arrow points to an area of the tissue that is dark red and appears to be surrounded by a thin rim of white tissue. This is consistent with the appearance of hemorrhage.I am confident in my opinion because I have extensive experience in diagnosing and treating cancer. I have seen many cases of hemorrhage, and I am familiar with the appearance of this condition.I would like to discuss my interpretation of the image with you. I believe that the image shows hemorrhage. I am confident that we can come to a consensus on the diagnosis. I am open to discussing my opinion further with the other medical experts, but I do not believe that it is necessary to talk to any other experts at this time. StepThe core premise of our MDAgent framework is its ability to adapt to the complexity of medical tasks. To validate this approach and gain insights into how LLMs perceive medical complexity compared to human experts, we conducted an annotation study. This study aimed to explore the alignment between LLMs and physicians in assessing medical question complexity, a critical factor in the effectiveness of our MDAgent framework.We selected 50 representative questions from the MedQA dataset, ensuring a balanced representation across USMLE steps 1, 2, and 3. This selection process aimed to cover a wide range of medical topics and complexity levels, mirroring the diverse challenges that our MDAgent framework is designed to address.Three physicians participated in our study: two with two years of Internal Medicine training (Post Graduate Year 2, PGY-2) and one general physician. This composition allowed us to capture a range of clinical perspectives. The physicians rated each question on a scale of -1 (low complexity), 0 (moderate complexity), and 1 (high complexity).Inter-rater Reliability To quantify the agreement among our physician raters, we employed Intraclass Correlation Coefficients (ICC). ICC is a widely used statistical measure in medical research for assessing the consistency of ratings among multiple raters. We specifically chose two ICC variants:• ICC2k (Two-way random effects, average measures): 0.269 [-0.14, 0.55]• ICC3k (Two-way mixed effects, average measures): 0.280 [-0.15, 0.57] ICC2k was selected because it assumes our raters are randomly selected from a larger population of similar raters, allowing for generalization of our findings. ICC3k, on the other hand, treats the raters as fixed, focusing on the consistency among our specific set of physicians.Both ICC values indicate moderate agreement among the raters. This level of agreement reflects the inherent complexity and subjectivity in evaluating medical questions, even among trained professionals. It also highlights the challenging nature of the task our MDAgent framework aims to address.Annotation Interface To facilitate the annotation process for both physicians and LLMs, we developed a specialized interface. This interface was designed to present medical questions in a clear and consistent manner, allowing for efficient and standardized complexity ratings. Figure 13 shows a screenshot of the annotation interface used in our study. To compare LLM performance with human expert judgments, we employed several state-of-the-art models to annotate the same set of questions. We then compared these assessments with the majority opinion of the physicians, determined by the mode of their ratings (or the mean in cases of complete disagreement). Justification: The abstract and introduction state the primary contributions of the paper, including the introduction of the MDAgents framework, its adaptive decision-making structure, and the significant performance improvements demonstrated through experimental results.Guidelines:• The answer NA means that the abstract and introduction do not include the claims made in the paper. • The abstract and/or introduction should clearly state the claims made, including the contributions made in the paper and important assumptions and limitations. A No or NA answer to this question will not be perceived well by the reviewers. • The claims made should match theoretical and experimental results, and reflect how much the results can be expected to generalize to other settings. • It is fine to include aspirational goals as motivation as long as it is clear that these goals are not attained by the paper.Detailed examples of low-, moderate-and high-complexity cases are provided in Appendix E.1. 


Decision: In this manuscript, the authors propose MDAgent, a multi-agent framework based on LLMs for medical decision making. The main novelty is a crafted collaboration scheme of multiple agents with designated roles, where a medical question is categorized into being of low, medium or high complexity. Based on the complexity checking result, MDAgents assigns a single primary care clinician LLM agent (for low complexity), a team of multidisciplinary LLM agents (for moderate complexity), or a team of integrated care LLM agent (for high complexity). The agent collaboration adopts multi-turn discussion and iterative report refinement. As much the question is complex, greater will be the benefit of multi-agent approach. 

The authors during the rebuttal provided satisfactory answers concerning the design of the empirical evaluation. The critical points were the extension of test sample (50 questions versus 100) and how realistic is the simulation of the clinical decision-making scenario (single-turn versus multi-turn interactions). They shared additional results, according to the request of the reviewer, as a proof of robustness for the property of the proposed model. The Reviewer recognized and acknowledged the additional effort of Authors in answering the questions.

This work indicates an interesting further research directions as follow-up of the empirical investigation: how to modulate the expertize of the team of agents in relation to the heterogeneity of expertise of physicians. The issue is how to balance the agreement/disagreement among physicians and the agreement/disagreement among the agents.

Despite this work presents ethical issues related to the critical health domain, e.g. hallucinations in complex situations, I believe that this contribution should be evaluated from the computational point of view and not from the perspective of deployment or provisioning as a service.

Reviews: review: paper_summary: paper_summary: The paper presents MDAgents, a multi-agent LLM system for answering medical questions, ranging from medical question answering and diagnostic reasoning to medical visual interpretation. The main novelty is a crafted collaboration scheme of multiple agents with designated roles, where a medical question is categorized into being of low, medim or high complexity. The question is then delegated to either individual specialized LLMs or teams of LMMs, and finally a decision is taken by a last LLM. The complete system does not focus on training or fine-tuning specialized medical agents, but rather use an appropriate foundational model and designated prompts to design the individual agents.

The approach is evaluated on ten medical diagnosis datasets, where 50 samples are used for testing. The authors compare their adaptive multi-agent system with a solo agent as well as a fixed group of agents, each with respective SoTA approaches. The results are based on GPT-4(V) or Gemini-Pro(Vision). The results show that MDAgents is either on-par or better than its competitors for all datasets.
strength_weakness: strength_weakness: - The paper could me more specific on method descriptions, e.g., multi-party chat / collaboration / report generation: The description is quite generic in the main paper and one only finds examples in the appendix. It is left open for me what implications the unknown design decisions have on the performance of the system. The same holds for, e.g., how the optimal number of collaborating agents is chosen on a case basis.
- The paper only uses 50 samples for inference without argumentation: It might be fine if other works to the same, but it should be clearly stated why 50 samples where chosen. Maybe the close competitors, e.g., MedAgents, do the same? What percentage is this for the respective datasets?
- Possible missing details on the evaluation setup: It might be that it is obvious or simply not used, but I am unsure when and where zero-shot or few-shot prompting is used. I see it for the low complexity cases for the recuriter LLM if the approach and also for solo competitors in the evaluation. Is it not used at other places? Extending on this point, maybe it would be helpful to emphasize such a point in the paper or point to it in the appendix for all used, established prompting strategies. If none is used in additition to the role descriptions and collaboratin schemes, it might be also worth noting.
questions: questions: - Please explain why, for the evaluation, you use 50 samples for inference and why it suffices / not have an impact on the relative results to the competitors.

- Please better explain, also in the paper, what a multi-party chat (mentioned in table 1) would look like for specific tasks. The case studies with MDAgents in the appending is insightful, but a better formalization/presentation of the topic in the main paper would be helpful.

- Are the expert discussion always one-to-one? If so, will there always be exhaustive one-to-ones?

- How dynamic is the system with respect to generating teams of experts of high severity cases? The meta analyses show that more agents are not necessarily better and that the system calibrates the number of collaborators, but how is it done in detail?

- Are the individual expert LLMs also variated (in terms of prompts)?

- Why was MedVidQA evaluated with Gemini-Pro(Vision)?

- To be sure my understanding is correct: are the individual LLMs mostly zero-shot learners? For few-shot learners (maybe only in the low complexity setting): how many dataset-specific examples, if any, are used for prompting in the evaluation?

### After author response ###
I appreciate the detailed answers. After additionally taking into account the answers and other reviews, I increase my score to accept, as the claims are now clearer backed up.
limitations: limitations: The authors discuss limitations to sufficient degree, including the lack of comparison to human clinicians or, most importantly, the danger of using systems that possibly hallucinate in critical situations.
paper_summary: paper_summary: In this paper, the authors propose MDAgent, a multi-agent framework for medical decision making. In this framework, the complexity of the problem is initially assessed by an agent. Based on this assessment, either a single agent or a group of agents is assigned to solve the problem. The authors evaluate their framework on 10 medical benchmarks, including both text-only and multi-modal datasets. Experimental results show that MDAgent outperforms existing baselines on 7 datasets and achieves high efficiency compared to other multi-agent methods.
strength_weakness: strength_weakness: - The description of MDT and ICT is unclear. For example, how are the prompts for each specialist prepared (the {{description}} in Agent initialization prompt, Appendix C.2), are they handcrafted or generated from LLM? How does the hierarchy shown in Figure 10 (c) and (d) work?
- The experiment shown in Figure 3 uses only 10 questions from a single dataset, which is not convincing. Additionally, subfigures (b), (c), and (d) are not mentioned in the analysis. What conclusions can be drawn from these results?
- The results shown in Figure 5 seem counterintuitive. For image + text and video + text, the score for low is higher than for moderate and high. This suggests that some questions that can be correctly solved with a single agent result in incorrect answers when multiple agents are involved. More analysis is needed to uncover the reason.
- The experiment incorporating the moderator's review and RAG shown in Table 3 has little relation to other parts of the paper. Considering that the moderator's review and RAG can also be combined with other multi-agent methods, the authors should compare the performance gain when attaching the moderator's review and RAG with different multi-agent frameworks if they want to demonstrate that their method is more suitable for the moderator's review and RAG.
questions: questions: - The authors could discuss more about the motivation behind the design of PCC, MDT, and ICT, considering that readers may not be familiar with the medical decision-making process. For example, why does MDT contain a multi-turn discussion but not ICT? Shouldn't the most complex questions require more discussion between agents?
- In section 4.3, the authors discuss Figure 5 in the first paragraph and Table 3 in the second paragraph. However, Table 3 appears on page 8 before Figure 5 appears on page 9, which could confuse readers.
- The formatting instructions for NeurIPS 2024 state that "All tables must be centered, neat, clean, and legible. The table number and title always appear before the table." However, Table 3 violates this rule.
limitations: limitations: - Most of the datasets used for evaluation are in multiple-choice question or true/false question format. In real-world scenarios, there are no options for doctors to choose from. Therefore, the authors could include some open-ended questions to simulate realistic applications.
paper_summary: paper_summary: This paper introduces a framework called MDAgents, which optimizes collaboration between multiple large language models (LLMs) for medical decision-making tasks. The main technical contribution of MDAgents is the deployment of a moderator agent to assess the complexity of incoming queries, categorizing them into low, moderate, and high difficulty. To improve efficiency, MDAgents either call single agents to solve low-complexity problems or use several agents to work together using real health studies-inspired collaboration schemes.
strength_weakness: strength_weakness: - The complexity assessment lacks details. More explanation on "low, moderate, high" is required.
- Additionally, the assessment is entirely determined by an LLM, raising concerns about whether poor judgment by the moderator agent may propagate.
- The experiments employing just 50 samples per dataset, may not adequately represent the performance of the proposed methods.
questions: questions: - MedAgents seems to use the same LLMs for all the agents, with the differences among the agents being the Agent Initialization Prompts. When the Multi-disciplinary Teams were recruited, is assigning an agent a role enough to let the LLM act like an expert in that specific discipline, which requires a lot of domain knowledge? How about equipping different agents with different knowledge for RAG?
- The observation that questions labeled as "Low" complexity have lower accuracy rates than "Moderate" ones in Figure 3 casts doubt on the reliability of the moderator's assessment. Also, the reasoning claim that the complexity assessment can increase accuracy by at least 80% is way more optimistic and seems like a really bold statement to me.
- In the ablation study, assigning all queries the highest complexity level results in reduced accuracy compared to the adaptive approach. Why is that? Does it mean calling multiple agents to collaborate in a complicated might not always lead to better outcomes, and incur higher API calls?
limitations: limitations: Addressed
paper_summary: paper_summary: This paper proposes MDAgents (Medical Decision-making Agents), a LLM collaboration framework for medical question answering. Given a single-modal or multi-modal medical question, MDAgents first classifies its complexity into low, moderate, and high. Based on the complexity checking result, MDAgents assigns a single primary care clinician LLM agent (for low complexity), a team of multidisciplinary LLM agents (for moderate complexity), or a team of integrated care LLM agent (for high complexity). The agent collaboration adopts multi-turn discussion and iterative report refinement. Evaluated on multiple medical QA datasets, MDAgents show better performance than a variety of baseline models, including other prompting strategies as well as other agent framework. Overall, this is an interesting paper.
strength_weakness: strength_weakness: 1. The main issue of this article is that the reported scores are not consistent with the literature, so it is unclear whether MDAgents is really state-of-the-art. For example, the original Medprompt paper reported their performance on MedQA as 90.2 and PubMedQA as 82.0. However, this paper reports Medprompt scores of 82.4 and 51.8 for these two datasets. The authors need to explain such discrepancy.
2. The complexity checker is something novel but is not well evaluated. The authors might need to sample a set of questions and ask human physicians to score its complexity (e.g., from 1-5), and report the correlation between LLM complexity and human complexity. 
3. Some figures in the results sections are confusing. I am not exactly sure what Figure 3 means, and it contains additional lines that should be removed. Additionally, for figure 5, does the "Low" mean the subset performance of questions classified as "Low", or the performance if all questions are classified as "Low"?
4. Studies on other medical agents (e.g., https://arxiv.org/abs/2402.13225) should also be discussed.
questions: questions: 1. What's the relative cost of MDAgents v.s. GPT-4 zero-shot CoT on each of the dataset?
2. If you remove the LLM complexity checker, and use the ICT method for all questions, will it achieve the highest result?
3. Can you aggregate the GPT-3.5 results like the main results table? They are currently scattered in different tables.
limitations: limitations: 1. The evaluations, while comprehensive, are mostly on multi-choice question answering tasks. This is not a realistic setting in medicine.

Comments: title: Thank you for the updates
comment: Thanks for the response. I will increase my score to acknowledge the authors' efforts in addressing my questions.
I appreciate the authors' answers to my questions and provided updates, and have no additional questions right now.
Thank you for your reply. I have updated my score.
Thank you for your response. I have increased my score.