ArticleNPJ digital medicine2025
Enhancing diagnostic capability with multi-agents conversational large language models.
Article in NPJ digital medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 53 papers.
What it found
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The trial behind it
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Who cites it
53 citing papers in PubMed.
- An LLM chatbot to facilitate primary-to-specialist care transitions: a randomized controlled trial.Nature medicine · 2026Trial
- AI in Proton Therapy Treatment Planning: A Narrative Review.International journal of particle therapy · 2026Article
- Performance of large language models in complex coronary artery disease decision-making and potential improvement strategy in a real-world cohort.BMJ health & care informatics · 2026Article
- On-premise medical AI agents for reliable clinical decision-making.Nature medicine · 2026Article
- Development of a Framework for Evaluating Large Language Model Safety and Reliability: a Proof-of-Concept Evaluation.Journal of medical systems · 2026Article
- MES: A Multi-Agent Evidence Synthesis System for Medical Decision-Making.medRxiv : the preprint server for health sciences · 2026Article
- CPS-Net (Collaborating Physicians in Silico Network): A Decentralized Multi-Agent Transformer Framework for Specialty-Aware Disease Prediction.Journal of medical systems · 2026Article
- A large language model-driven multidisciplinary AI agent system predicts delirium in emergency critically ill patients.Cell reports. Medicine · 2026Article
- Generative large language models in medicine: a scoping review of recent methodological advances.npj health systems · 2026Review
- MediHive: A Decentralized Agent Collective for Medical Reasoning.Journal of healthcare informatics research · 2026Article
- A Multidisciplinary Team-Based Large Language Model Framework for Predicting Postoperative Neurological Complications in Acute Type A Aortic Dissection: Model Development and Validation Study.Journal of medical Internet research · 2026Article
- Role prompting modulates linguistic style but not clinical decision structure in GPT-5 tumour board simulation.NPJ digital medicine · 2026Article
- TCM-Agent: Advancing network pharmacology and herbal medicine discovery with LLM-based multi-agent systems.Journal of pharmaceutical analysis · 2026Article
- Pathology-CoT: learning visual chain-of-thought agents from expert whole-slide image diagnosis behaviour.Nature biomedical engineering · 2026Article
- Review
- Diagnostic accuracy and citation integrity of four large language models on otolaryngology vignettes.European archives of oto-rhino-laryngology : official journal of the European Federation of Oto-Rhino-Laryngological Societies (EUFOS) : affiliated with the German Society for Oto-Rhino-Laryngology - Head and Neck Surgery · 2026Article
- A comprehensive survey of AI agents in healthcare.Journal of biomedical informatics · 2026Review
- Hard to Halt: Automation Bias in Agent-Driven Sequencing Prior Authorization Workflows.medRxiv : the preprint server for health sciences · 2026Article
- Improving Radiology Report Error Detection Using a Multipass Large Language Model: Framework Development and Validation.JMIR medical informatics · 2026Article
- Full-Body AI Agent: A Perspective on Multi-Scale Collaborative AI for Systemic Biology and Precision Medicine.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Review
Corrections and comments
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Authors and funding
14 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Large Language Models (LLMs) show promise in healthcare tasks but face challenges in complex medical scenarios. We developed a Multi-Agent Conversation (MAC) framework for disease diagnosis, inspired by clinical Multi-Disciplinary Team discussions. Using 302 rare disease cases, we evaluated GPT-3.5, GPT-4, and MAC on medical knowledge and clinical reasoning. MAC outperformed single models in both primary and follow-up consultations, achieving higher accuracy in diagnoses and suggested tests. Optimal performance was achieved with four doctor agents and a supervisor agent, using GPT-4 as the base model. MAC demonstrated high consistency across repeated runs. Further comparative analysis showed MAC also outperformed other methods including Chain of Thoughts (CoT), Self-Refine, and Self-Consistency with higher performance and more output tokens. This framework significantly enhanced LLMs' diagnostic capabilities, effectively bridging theoretical knowledge and practical clinical application. Our findings highlight the potential of multi-agent LLMs in healthcare and suggest further research into their clinical implementation.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.