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Ontology-guided multi-agent reasoning for explainable differential diagnosis
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arXiv AI
5d ago
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 AI
Jun 16
MedCollab: IBIS-Guided Multi-Agent Collaboration with Hierarchical Disease Relation Chains for Clinical Diagnosis
arXiv:2603. 01131v3 Announce Type: replace-cross Abstract: Clinical diagnosis is a gradual process of evidence integration, in which physicians move from symptoms and medical history to examinations, competing hypotheses, disease relations, and treatment decisions.
arXiv AI
Jul 20
GraphDx: A Cost-Aware Knowledge-Enhanced Multi-Agent Framework for Sequential Diagnosis
arXiv:2607. 15280v1 Announce Type: new Abstract: Sequential diagnosis requires balancing diagnostic accuracy against resource costs through iterative information gathering.
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
Sep 15
EMR: Self-Evolving Medical Multi-Agent System via Experience Mining and Reuse
arXiv:2609.15161v1 Announce Type: cross Abstract: Large language model (LLM) driven multi-agent systems have shown promise in complex clinical reasoning, yet existing approaches rely on static strate...