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

We sincerely appreciate your thoughtful review and the valuable insights you provided. Your feedback helps us clarify key aspects of our research and improve the overall quality of our submission.

W1. The reported scores not consistent with the literature

Thank you for pointing out the discrepancies in the reported scores. We appreciate your attention to this detail and would like to clarify the differences between our experimental setup and the original paper [1] that introduced Medprompt:

1) Dataset Differences: The high accuracy reported by [1] for Medprompt was achieved using the MedQA-US dataset with 4-option questions. Our evaluation, however, was conducted on the MedQA-US dataset with 5-option questions, which is inherently more challenging and likely contributes to the lower accuracy.

2) Implementation Variations: In the original Medprompt implementation, they utilized five kNN-selected few-shot exemplars with a 5x ensemble. For our experiments, we used three exemplars to ensure fair comparisons with other methods in our main experiments. We initially considered using a different number of exemplars to calibrate our implementations further. However, we chose a smaller number due to the increased cost and time required for comprehensive testing. Under similar conditions, such as using more exemplars like [1], we anticipate that our MDAgents and other baseline methods might also demonstrate improved performance. We will ensure these distinctions in the implementations are clearly outlined in our paper to provide proper context for the reported results.

W2. What is the correlation between LLM complexity scores and human Physician's judgments?

We have addressed this in the general response by detailing a study where three human physicians annotated question complexity and conducted a correlation analysis with LLM assessments.

W3 & Q2. Figure 3 and 5 confusing and what if we remove LLM complexity checker in the ablation?

For Figure 5, "Low," "Moderate," and "High" denote the performance outcomes when all questions in the dataset are manually set into each respective complexity category, rather than relying on the complexity obtained by the LLM complexity checker. This means:

• "Low" shows the accuracy when all questions are set to low complexity.
• "Moderate" indicates the accuracy when all questions are set to moderate complexity.
• "High" reflects the accuracy when all questions are set to high complexity.

This setup was designed to evaluate how the model's performance varies when operating under uniform complexity assumptions across the dataset. We will ensure that the figure description and ablation setup in our paper (Section 4.3) are updated to clearly explain this methodology.

W4. Studies on other medical agents should also be discussed

We will include a discussion of the study [1] suggested and others [2,3,4,5] to better contextualize our work and clarify how MDAgents compare with existing approaches in medical decision-making.

Q1. What is the relative cost of MDAgents vs. gpt-4 zero-shot CoT on each dataset

As detailed in Table 3 of the attached pdf file, MDAgents require higher costs across the datasets compared to Zero-shot CoT. Our methods use a 3-shot setting across different medical complexities and recruit multiple agents, which are needed to effectively handle the complexity of medical datasets which contributes to the enhanced performance.

Q3. Need to aggregate GPT-3.5 results in Table 3

We will aggregate the GPT-3.5 results into the main results table for clarity and easier comparison with other models.

L1. Evaluations limited to multi-choice question tasks which is not a realistic medical setting

To address the issue, we conducted additional experiments using the MedQA dataset without predefined options, aiming to better mirror the open-ended nature of clinical decision-making.

Recognizing that real-world medical scenarios often involve complex, multi-turn interactions, we are committed to refining our evaluation methods to more accurately simulate actual clinical conditions. In our latest experiments with the gpt-4o-mini model, we evaluated our method alongside 3-shot CoT-SC and Reconcile, using 100 samples from the MedQA dataset in an open-ended format. The results were as follows:

• 3-shot CoT-SC: 52 % accuracy
• Reconcile: 40 % accuracy
• Ours: 56 % accuracy

These results indicate that our approach performs competitively even in more realistic settings, underscoring its potential in clinical applications. We are open to further suggestions and welcome any recommendations for additional datasets or evaluation frameworks that could enhance the realism and robustness of our assessments.

We believe our responses have addressed your concerns and provided clarity. Please let us know if you require any additional information or further clarifications for your re-evaluation.

References

[1] Nori, H., Lee, Y. T., Zhang, S., Carignan, D., Edgar, R., Fusi, N., ... & Horvitz, E. (2023). Can generalist foundation models outcompete special-purpose tuning? case study in medicine.

[2] Jin, Q., Wang, Z., Yang, Y., Zhu, Q., Wright, D., Huang, T., … & Lu, Z. (2024). AgentMD: Empowering Language Agents for Risk Prediction with Large-Scale Clinical Tool Learning.

[3] Li, J., Wang, S., Zhang, M., Li, W., Lai, Y., Kang, X., ... & Liu, Y. (2024). Agent hospital: A simulacrum of hospital with evolvable medical agents.

[4] Fan, Z., Tang, J., Chen, W., Wang, S., Wei, Z., Xi, J., ... & Zhou, J. (2024). Ai hospital: Interactive evaluation and collaboration of llms as intern doctors for clinical diagnosis.

[5] Yan, W., Liu, H., Wu, T., Chen, Q., Wang, W., Chai, H., ... & Zhu, L. (2024). ClinicalLab: Aligning Agents for Multi-Departmental Clinical Diagnostics in the Real World.

We appreciate your detailed review and the opportunity to refine our work based on your feedback. Your suggestions are crucial in guiding our efforts to provide a more comprehensive analysis and evaluation.

W1 & W2. The complexity assessment lacks details and concerns about judgment made by the moderator agent may propagate.

We address this issue in Section 4.3 through an ablation study, the results of which are presented in Figure 5. This study evaluates the effectiveness of our adaptive complexity selection mechanism against static assignments across different modalities.

Our findings indicate that the adaptive method significantly outperforms static settings, demonstrating robustness in our approach to complexity assessment and reducing the potential for error propagation. Additionally, a detailed comparison of human doctor annotations with the LLM’s assessments is provided in the pdf file attached.

W3. 50 Samples per dataset may not show true method performance

Please refer to the general response and the pdf file attached for extra experiments with N=100 samples for all datasets and with entire test samples for the MedQA dataset.

Q1. Is assigning an agent a role enough for LLMs to act like experts? What if equipping different knowledge with RAG?

Thank you for your idea about the knowledge initialization for agents using RAG in our MDAgents framework. To address your point on domain expertise, we conducted extra experiments on top of Table 3:

MDAgents: 71.8%

+ MedRAG: 75.2%

+ Medical Knowledge Initialization: 76.0% (your suggestion)

+ Moderator’s Review: 77.6%

+ Moderator’s Review & MedRAG: 80.3%

These results indicate that augmenting agents with specific knowledge and structured reviews have potential to improve their ability to simulate domain expertise. We will detail these findings in our revised manuscript.

Q2. Doubts on Complexity Labels and Optimistic Accuracy Claims in Figure 3

To re-explain Figure 3, we need to explain why this experiment is needed, which will help to understand the meaning of Figure 3.

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 Interdisciplinary Doctor Team, 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 the appropriate difficulty for each question corresponds to the difficulty level at which the probability of correctly solving the question is highest, as we cannot incorporate doctor in the difficulty decision making (while we also have ablation studies with human doctors as well)

To determine this, we assessed the accuracy of solutions across various difficulty levels. Specifically, we evaluated 10 medical problems (increased to 25 after rebuttals) by solving each problem 10 times at each difficulty level. By measuring the success rate, we aimed to identify the difficulty level that yielded the highest accuracy.

This rigorous approach ensures that the LLM's classification of problem difficulty aligns with the most effective and accurate medical solutions, thereby optimizing the application of medical expertise to each question.

Going back to Reviewer mJeV’s question,

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.

It is important to clarify that Figure 3(b) does not indicate specific questions labeled as "Low" complexity. Instead, it shows the probability that the LLM can correctly answer questions when c our “Low Complexity” solution is applied for all questions. This explanation extends to Figures 3(c) and 3(d) as well. Figure 3(a), on the other hand, illustrates whether the LLM is choosing the difficulty level that provides the highest accuracy.

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.

We did not make such a claim. Our assertion is that the LLM is automatically selecting the appropriate difficulty level with an accuracy rate close to 80%.

Q3. Why does assigning high complexity to all queries reduce accuracy and increase API costs?

To address this issue, we conducted additional experiments to see if we missed the benefit to improve the performance in high complexity cases.

For the image+text scenario, we explored various collaborative settings and found these outcomes:

• Sequential & No Discussion: 39.0%

• Sequential & Discussion: 45.0%

• Parallel & No Discussion: 56.0%

• Parallel & Discussion: 59.0%

This indicates the importance of multi-turn discussions, particularly in complex cases and the exclusion of this feature likely contributed to lower performance.

We hope our detailed responses have addressed your concerns effectively. Please feel free to add follow-up questions for further clarifications or updates needed for your re-evaluation.

Thanks for the response. I will increase my score to acknowledge the authors' efforts in addressing my questions.

We appreciate your review and the opportunity to address your concerns. We have conducted numerous additional experiments to validate our approach and enhance the robustness of our findings.

W1. The description of MDT and ICT is unclear.

• Agent Initialization: In our framework, the roles and descriptions for specialists within the MDT and ICT are dynamically determined by a specialized recruiter LLM. This process automates the generation of role-specific prompts, ensuring each agent is precisely tailored to the demands of the case.

• Hierarchy Explanation: The hierarchical configuration in MDT and ICT, as depicted in Figure 10 (c-d), is inspired by traditional clinical reporting structures. This protocol ensures structured communication flow and oversight, preventing information discrepancies and fostering coherent team collaboration.

W2. Experiment shown in Figure 3 with only 10 questions not convincing

We acknowledge the limitation of using only 10 questions in Figure 3. Initially, we had 10 questions because this already required generating 300 question-answer pairs (10 solutions * 3 difficulty levels * 10 questions). However, with the current significant reduction in price offered by gpt-4o-mini, we added 15 more questions which results in 750 question-answer pairs.

We will expand our experiments to include more questions from multiple datasets to provide a more robust evaluation.

W3. The results in Figure 5 seem counterintuitive

In response to your concerns, we have conducted additional experiments and analyses to clarify these outcomes.

For the image+text scenario, further experiments with different settings has revealed the following results:

• Sequential & No Discussion: 39.0%
• Sequential & Discussion: 45.0%
• Parallel & No Discussion: 56.0%
• Parallel & Discussion: 59.0%

These results suggest that the integration of multi-turn discussions substantially benefits the decision-making process, particularly in complex cases. The initial absence of this feature in our methodology likely contributed to the earlier lower performance figures.

For the video+text scenario, the use of the deprecated gemini-pro vision model initially restricted multi-turn chats (multi-modal cases). By summarizing the video content with one agent and then re-initializing another for further multi-turn discussions, we attempted to overcome this limitation. We assume that this approach might have influenced the accuracy in the moderate and high complexity scenarios.

To thoroughly address these issues and refine our understanding, we plan to further conduct detailed experiments focused on:

• Investigating the impact of different agent initialization strategies on the consistency and accuracy of outcomes (with gemini-1.5 flash).

We will ensure that these additional analyses are included in the revised manuscript for the camera-ready version, aiming to provide a more comprehensive understanding of the interplay between model capabilities and task complexities.

W4. Need to Compare Moderator's Review and RAG Integration with Other Multi-Agent Frameworks to Demonstrate Suitability

Our primary focus was on demonstrating how the MDAgents framework can enhance medical decision-making through adaptive collaboration structures with initial complexity classification. The inclusion of the moderator's review and Retrieval-Augmented Generation (RAG) in Table 3 was intended to show the potential improvements in accuracy when integrating external knowledge sources and structured review processes.

While we recognize that the moderator's review and RAG could be applied to other multi-agent frameworks, our aim was to illustrate their effectiveness specifically within MDAgents. However, it's important to note that RAG itself is not a medical-specific technique. We focused on demonstrating how the integration of RAG and structured reviews can be particularly effective within a medical-aware structured adaptive multi-agent system compared to a naive multi-agent system.

Q1. Discuss more about the motivation behind the design of PCP, MDT, and ICT, and why doesn't ICT include multi-turn discussions?

The design of PCP, MDT and ICT represents the real-world clinical decision making processes which is mostly dependent on the complexity of the medical cases (refer to Appendix Section D.1.1. for real-world examples). If the case or task is with low complexity where PCP could solve it without consulting specialists (Case 1, Appendix D.1.1), if it is moderate complexity PCP might have to consult to a specialist agents (Case 2, Appendix D.1.1.), and lastly if the case is complex so that it involves multi-disciplinary consult (Case 3, Appendix D.1.1) we let multidisciplinary agents interact with one another.

Additional experimental results with ICT setting (Accuracy):

• Sequential report generation w/ discussion: 84%
• Sequential report generation w/o discussion: 78% (Our previous approach)
• Parallel report generation w/ discussion: 82%
• Parallel report generation w/o discussion: 80%

The results indicate that incorporating discussions among lead clinicians in ICT enhances decision-making accuracy, particularly in sequential report generation. This evidence supports your point that complex cases benefit from more extensive deliberation.

In the updated manuscript, we will adjust the ICT model to include more robust discussion protocols.

Q2 & Q3. The order of Table 3 Appearing Before Figure 5 confuses readers and Table 3 violates NeurIPS 2024 formatting rules

In the final version, we will ensure that the figures and tables are presented in the same order as they are discussed in the text to enhance clarity for the readers and revise it to comply with the guidelines in the updated manuscript.

We believe our extensive additional experiments and clarifications have addressed your queries. Please let us know if further details are required to support the re-evaluation of our work.

We appreciate your thoughtful review and the recognition of our paper’s potential contributions to the field. Your insights are invaluable in guiding us to enhance our work and clarify the findings.

W1. & Q2. Lack of detailed descriptions and examples

We recognize the importance of providing more detailed descriptions to clarify our methods, particularly in the areas of multi-party chat, collaboration, and report generation. In the revised version, we will enhance our explanations and include visual aids for better understanding.

• Number of Agents: We will explain in Section 3.4 how the recruiter LLM determines the optimal number of agents, considering task complexity and expertise, as outlined in Appendix C.2.

• Report Generation: We will expand on the report generation process in Section 3.4 by including the specific prompt used and detailing how information is synthesized from multiple agents, building on the example in Figure 12.

• Multi-Party Chat and Collaboration: We will provide a more comprehensive description of agent interactions and collaboration dynamics in the main text, focusing on their role in fostering effective teamwork, as illustrated in Figure 11.

W2. & Q1. Why use only 50 samples in the experiments?

We selected 50 samples to maintain consistency with similar studies, such as [1], which employ a sample size 50 for evaluation. This decision was made to ensure a manageable yet statistically significant analysis with three random seeds, aligning with our experiment budget constraints (experiments cost listed in Table 3 in attached pdf file).

In our experiments, we utilized the gpt-4 (vision) model, which is approximately up to 200 times more expensive than the gpt-4o-mini model. Given this consideration, we increased the number of samples to 100 with gpt-4o-mini and reproduced our experimental results to provide a more comprehensive evaluation (Table 1 in attached pdf file).

In particular, for MedQA, we evaluated the entire test set to demonstrate that performance trends remain consistent across different models, thus reinforcing the reliability of our findings (Table 2 in attached pdf file). We believe this approach balances practical constraints with the need for rigorous evaluation and hope this addresses your concerns.

Additionally, it is important to note that our use of multiple datasets reflects our intention to test the multi-agent system across various medical scenarios, acknowledging the diversity and complexity of medical diagnostics. By selecting a smaller number of samples from each of the ten datasets, we aim to capture a broad spectrum of medical conditions and scenarios, making our study comparative and comprehensive relative to others..

Lastly, the percentage that these 100 samples represent of the total test set sizes varies, which can be calculated based on Table 5 in our Appendix:

• MedQA: 0.0078%
• PubMedQA: 20%
• DDXPlus: 0.074%
• SymCat: 0.027%
• JAMA: 6.56%
• MedBullets: 32.47%
• MIMIC-CXR: 6.53%
• PMC-VQA: 200%
• Path-VQA: 0.00295%
• MedVidQA: 64.52%

W3. & Q7. Details missing in evaluation setup and prompting strategies

In our framework, we employed a 3-shot setting for low-complexity cases where PCP agents make decisions. For moderate and high-complexity cases involving multi-LLM agents, we used a zero-shot setting. We will update our experimental setup in the revised paper to clearly indicate where zero-shot and few-shot prompting are used, and we will add this notation to the relevant figures.

Q3. Is expert discussion one-to-one?

In our implementation, we allowed the LLMs to decide if they wanted to engage with other agents. Rather than limiting interactions to one-to-one, we facilitated many-to-many discussions, where multiple agents could interact simultaneously. We will add more detailed descriptions in Section 3.4 and clarify this aspect in Figure 2.

Q4. How does the system dynamically calibrate expert teams for high severity cases?

The system dynamically calibrates expert teams based on the complexity of the medical query. Initially, the recruiter LLM determines the number of agents allocated to each team. For the meta-analysis, we fixed the number of agents to assess the impact on performance, rather than allowing the recruiter LLM to decide dynamically. In our implementation, instead of removing the agents during discussions, we ask each LLM agent to participate actively by contributing when they have relevant insights or corrections.

For moderate complexity case, agents could engage in discussions to clarify or emphasize points in each round / turn. In high complexity case, a lead clinician agent guided the process, asking assistant LLMs for specific investigations and deciding which agents to consult based on their expertise.

Q5. Are Individual Expert LLMs Variated by Prompts?

Yes, we assigned specific roles to the LLMs with detailed descriptions during the initialization step. These roles were determined by the recruiter LLM, to give LLM a clear function, which allowed for varied interactions based on the prompts. The initialization prompt is detailed in Appendix C.2, where we show the Agent initialization prompt.

Q6. Why was MedVidQA evaluated with Gemini Pro Vision?

We evaluated MedVidQA with Gemini-Pro Vision because it was the only model capable of effectively handling both text and video inputs with reasonable performance. Alternatively, we can sample frames from the videos and transcribe (e.g. whisper) the spoken text to provide images and text to vision LLMs (e.g. gpt-4v), but Gemini-Pro Vision offered a more integrated solution for video input.

We hope our responses have addressed your concerns. Please do not hesitate to let us know if any further explanations or updates are needed to assist in the re-evaluation of our paper.

Reference

[1] Nori et al. (2023). Can generalist foundation models outcompete special-purpose tuning? case study in medicine.