Multi-Agent Medical Debate System for Clinical Reasoning

Abstract

Clinical deployment of large language models (LLMs) for medical question answering faces three primary challenges: (1) models frequently produce unsupported or hallucinated claims because their parametric knowledge is not verified against biomedical evidence; (2) single-pass inference prevents iterative self-correction, leading to incomplete differential diagnoses; and (3) poorly calibrated confidence scores undermine clinician trust. To address these challenges, we introduce a retrieval augmented multi-agent model whose components are specifically designed to generate medical answers through three stages: hypothesis generation, adversarial debate, and independent judgment, wherein evidence conditioned hypotheses are generated, contested by an adversarial process, and finally evaluated by an independent model. Each of these models utilizes a semantic retrieval system to access medical literature from PubMed, explanations of answer benchmarks from MedQA, and medical records from MIMIC III. The model is evaluated using the MedQA USMLE benchmark and a collection of prompts created from MIMIC III data, as well as by comparing the performance of the system against a general purpose LLM and a fine tuned medical LLM. Results indicate that the approach described improves the supported sentences rate, the precision and recall of the attributed sentences, and the groundedness of the answers provided while also reducing the number of unsupported claims, hallucinations, Brier score, and expected calibration error, while maintaining the same accuracy in medical diagnosis. Thus, these findings indicate that the incorporation of adversarial and verification models during inference can improve the model’s ability to align with the evidence and to calibrate uncertainty in its predictions without requiring any adaptation to the models with supervised training data.