MedAgents: Large Language Models as Collaborators for Zero-shot Medical Reasoning

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Abstract

Large language models (LLMs), despite their remarkable progress across various general domains, encounter significant barriers in medicine and healthcare.This field faces unique challenges such as domain-specific terminologies and reasoning over specialized knowledge.To address these issues, we propose MEDAGENTS, a novel multi-disciplinary collaboration framework for the medical domain.MedAgents leverages LLM-based agents in a role-playing setting that participate in a collaborative multi-round discussion, thereby enhancing LLM proficiency and reasoning capabilities.This training-free framework encompasses five critical steps: gathering domain experts, proposing individual analyses, summarising these analyses into a report, iterating over discussions until a consensus is reached, and ultimately making a decision.Our work focuses on the zeroshot setting, which is applicable in realworld scenarios.Experimental results on nine datasets (MedQA, MedMCQA, PubMedQA, and six subtasks from MMLU) establish that our proposed MEDAGENTS framework excels at mining and harnessing the medical expertise within LLMs, as well as extending its reasoning abilities.Our code can be found at https: //github.com/gersteinlab/MedAgents. Expert Gathering Analysis Proposition Report Summarization Collaborative Consultation Decision Making A 66-year-old male with a history of heart attack and recurrent stomach ulcers is experiencing persistent cough and chest pain, and recent CT scans indicate a possible lung tumor.Designing a treatment plan that minimizes risk and maximizes outcomes is the current concern due to his deteriorating health and medical history....... ...... .

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