arXiv AI
EMR: Self-Evolving Medical Multi-Agent System via Experience Mining and Reuse
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arXiv AI
Aug 25
MACD: Multi-Agent Clinical Diagnosis with Self-Learned Knowledge for LLM
The paper introduces MACD, a Multi-Agent Clinical Diagnosis framework that enables large language models to self‑learn clinical knowledge through a multi‑agent pipeline of summarization, refinement, and application. MACD is extended into a human‑AI collaborative workflow where multiple diagnostician agents consult iteratively, guided by a judge agent and human oversight. Evaluation on the MIMIC‑MACD cohort shows significant gains in diagnostic accuracy—an average 11.6 percentage‑point improvement over authoritative knowledge for open‑weight LLMs and an 18.3‑percentage‑point boost over physician‑only diagnosis in text‑only vignettes.
arXiv AI
Aug 13
Social Chain of Thought: A Multi-Agent Architecture Grounded in Medical Differential Diagnosis Methodology
arXiv:2608. 11420v1 Announce Type: new Abstract: Medical diagnostic reasoning is a high-impact use case for LLMs that carries significant implications for the health and wellbeing of users.
arXiv AI
Jul 10
Aligning Clinical Needs and AI Capabilities: A Survey on LLMs for Medical Reasoning
arXiv:2607. 07761v1 Announce Type: new Abstract: Large language models (LLMs) have emerged as important tools in healthcare, showing growing potential for clinical reasoning and patient care.
arXiv AI
Oct 28
Ontology-guided multi-agent reasoning for explainable differential diagnosis
The paper introduces an ontology‑guided, multi‑agent framework for differential diagnosis that mirrors physicians’ iterative reasoning. By grounding inference in a structured, expert‑curated medical knowledge base, the system produces explicit, auditable reasoning steps, reduces hallucination, and improves transparency. It also identifies key missing symptoms and generates targeted follow‑up questions to lower diagnostic uncertainty.
arXiv Computer Vision
Aug 25
MSM-Mem: A Universal Medical Structured Multimodal Memory Framework for Medical AI Agents
arXiv:2608.21810v1 Announce Type: cross Abstract: Clinical decision-making is inherently experience-driven: physicians progressively refine their reasoning by synthesizing patient history, multimodal...
arXiv AI
Oct 27
ClinVerse: A Realistic Multi-Agent Diagnostic Environment for Training and Evaluating Medical Agents
arXiv:2610.34878v1 Announce Type: new Abstract: Disease diagnosis is a key application of large language model (LLM) agents in healthcare. However, existing medical agents are primarily trained and e...