# Enhancing Diagnostic Accuracy through Multi-Agent Conversations: Using Large Language Models to Mitigate Cognitive Bias arXiv: https://arxiv.org/abs/2401.14589 ยท HTML: https://mlsanity.com/paper/2401.14589/ ## Key facts - Cited by 20 papers in the map (data through 14 Oct 2026). - Placed in Medical and scientific domain agents, one of 16 directions in LLM agents and tool use. - Builds on 7 of its 47 references that are in the map. - First posted on arXiv 26 Jan 2024. ## Placed in - LLM agents and tool use / [Medical and scientific domain agents](https://mlsanity.com/agents/medical-and-scientific-domain-agents/) ## Builds on (most cited first, 7 of 7) - [AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversation](https://mlsanity.com/paper/2308.08155/) (2023) - [Large Language Models Encode Clinical Knowledge](https://mlsanity.com/paper/2212.13138/) (2022) - [Can Generalist Foundation Models Outcompete Special-Purpose Tuning? Case Study in Medicine](https://mlsanity.com/paper/2311.16452/) (2023) - [Role-Play with Large Language Models](https://mlsanity.com/paper/2305.16367/) (2023) - [Interactive Natural Language Processing](https://mlsanity.com/paper/2305.13246/) (2023) - [Self-Adaptive Large Language Model (LLM)-Based Multiagent Systems](https://mlsanity.com/paper/2307.06187/) (2023) - [Ascle: A Python Natural Language Processing Toolkit for Medical Text Generation](https://mlsanity.com/paper/2311.16588/) (2023) ## Built on by (most cited first, 20 of 20) - [MultiAgentBench: Evaluating the Collaboration and Competition of LLM agents](https://mlsanity.com/paper/2503.01935/) (2025) - [A Comprehensive Survey in LLM(-Agent) Full Stack Safety: Data, Training and Deployment](https://mlsanity.com/paper/2504.15585/) (2025) - [KG-Rank: Enhancing Large Language Models for Medical QA with Knowledge Graphs and Ranking Techniques](https://mlsanity.com/paper/2403.05881/) (2024) - [Advancing Conversational Diagnostic AI with Multimodal Reasoning](https://mlsanity.com/paper/2505.04653/) (2025) - [Enhancing LLMs for Impression Generation in Radiology Reports through a Multi-Agent System](https://mlsanity.com/paper/2412.06828/) (2024) - [AGENTiGraph: A Multi-Agent Knowledge Graph Framework for Interactive, Domain-Specific LLM Chatbots](https://mlsanity.com/paper/2508.02999/) (2025) - [AGENTiGraph: An Interactive Knowledge Graph Platform for LLM-based Chatbots Utilizing Private Data](https://mlsanity.com/paper/2410.11531/) (2024) - [A Multimodal Multi-Agent Framework for Radiology Report Generation](https://mlsanity.com/paper/2505.09787/) (2025) - [Multi-Agent Debate Strategies to Enhance Requirements Engineering with Large Language Models](https://mlsanity.com/paper/2507.05981/) (2025) - [MoodAngels: A Retrieval-augmented Multi-agent Framework for Psychiatry Diagnosis](https://mlsanity.com/paper/2506.03750/) (2025) - [Planted in Pretraining, Swayed by Finetuning: A Case Study on the Origins of Cognitive Biases in LLMs](https://mlsanity.com/paper/2507.07186/) (2025) - [Retrieval-Augmented Generation for Generative Artificial Intelligence in Medicine](https://mlsanity.com/paper/2406.12449/) (2024) - [Multi-Agent Debate Strategies: Survey, Taxonomy, and Challenges](https://mlsanity.com/paper/2607.26212/) (2026) - [Analyzing Information Sharing and Coordination in Multi-Agent Planning](https://mlsanity.com/paper/2508.12981/) (2025) - [A Multi-Agent System for Complex Reasoning in Radiology Visual Question Answering](https://mlsanity.com/paper/2508.02841/) (2025) - [Beyond MedQA: Towards Real-world Clinical Decision Making in the Era of LLMs](https://mlsanity.com/paper/2510.20001/) (2025) - [Sparse Multi-Stage Expert-Agent Routing for Complex Clinical Reasoning](https://mlsanity.com/paper/2608.21948/) (2026) - [When Pigs Get Sick: Multi-Agent AI for Swine Disease Detection](https://mlsanity.com/paper/2503.15204/) (2025) - [AgenticSum: An Agentic Inference-Time Framework for Faithful Clinical Text Summarization](https://mlsanity.com/paper/2602.20040/) (2026) - [Characterizing Rhetorical Misalignment in Decision-Making with Language Models](https://mlsanity.com/paper/2608.14630/) (2026) ## Abstract (arXiv) Background: Cognitive biases in clinical decision-making significantly contribute to errors in diagnosis and suboptimal patient outcomes. Addressing these biases presents a formidable challenge in the medical field. Objective: This study explores the role of large language models (LLMs) in mitigating these biases through the utilization of a multi-agent framework. We simulate the clinical decision-making processes through multi-agent conversation and evaluate its efficacy in improving diagnostic accuracy. Methods: A total of 16 published and unpublished case reports where cognitive biases have resulted in misdiagnoses were identified from the literature. In the multi-agent framework, we leveraged GPT-4 to facilitate interactions among four simulated agents to replicate clinical team dynamics. Each agent has a distinct role: 1) To make the final diagnosis after considering the discussions, 2) The devil's advocate and correct confirmation and anchoring bias, 3) The tutor and facilitator of the discussion to reduce premature closure bias, and 4) To record and summarize the findings. A total of 80 simulations were evaluated for the accuracy of initial diagnosis, top differential diagnosis and final two differential diagnoses. Results: In a total of 80 responses evaluating both initial and final diagnoses, the initial diagnosis had an accuracy of 0% (0/80), but following multi-agent discussions, the accuracy for the top differential diagnosis increased to 71.3% (57/80), and for the final two differential diagnoses, to 80.0% (64/80). Conclusions: The framework demonstrated an ability to re-evaluate and correct misconceptions, even in scenarios with misleading initial investigations. The LLM-driven multi-agent conversation framework shows promise in enhancing diagnostic accuracy in diagnostically challenging medical scenarios.