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Research questionHow can language models reason iteratively to diagnose complex clinical cases safely and accurately?Single-turn question answering may not reflect the iterative, role-based reasoning used in complex diagnosis. This makes it difficult for language models to identify likely diagnoses while maintaining safety on rare or challenging cases.
AI
AI Agents
Evaluation & Benchmarks
Health
Multi-agent Systems
Reasoning
Latest papersRecent research connected to this question, newest first.A Structured Debate-Mixture-of-Agents Framework for Complex Clinical Diagnostic Decision SupportThe evidence comes from DMoA, a structured role-based multi-agent framework evaluated against a GPT-4o baseline on 297 rare-disease cases and 1,719 challenging cases. Reported gains covered most-likely-diagnosis accuracy and safety rate; ablations examined the effects of structured workflow, model count, output length, framework structure, base model, and token budget. The supplied evidence is limited to these case-based evaluations.research paper · Sep 4, 2026
Related questions
How can we evaluate LLM clinical reasoning across multilingual, multimodal clinical time series?How can LLMs improve rare-disease diagnosis while preserving ontology-based evidence trails?How can we quantify and reduce divergent, nonsensical reasoning in large language models?How can clinical decision systems remain accurate and auditable under scarce, imbalanced data and changing features?