Two Minds are Safer Than One: Argumentative Llm Agents for Clinical Diagnosis

Abstract

Large Language Models (LLMs) show strong potential for clinical reasoning, yet their deployment in medical decision support is hindered by hallucinations, overconfidence, and limited transparency. We propose Dialectic Diagnosis, an agentic framework in which two heterogeneous LLM agents engage in structured argumentative interaction inspired by clinical second-opinion workflows. A Clinical Reasoner proposes candidate diagnoses, while a Skeptical Critic challenges these hypotheses by identifying omissions, cognitive biases, and unsupported reasoning. Their interaction is governed by a formal finite-state machine (FSM) enforcing a disciplined proposecritique-resolve protocol, with final decisions produced by an Arbiter agent providing calibrated confidence estimates. To ensure transparency, we introduce a Diagnostic Argument Graph that explicitly represents supporting evidence, contradictions, and missing diagnoses. Evaluations on real-world clinical datasets (MIMIC-IV and eICU) show clear gains over single-agent LLM baselines, with improved diagnostic accuracy, lower calibration error, and fewer critical diagnostic omissions. These results indicate that structured argumentative interaction between LLM agents provides a principled path toward safer and more explainable clinical AI systems.