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.
By Roxana Ramona Szomiu, George Ursu, Adrian Groza
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.
By Yuqi Zhan, Xinyue Wu, Tianyu Lin, Yutong Bao, Xiaoyu Wang, Weihao Cheng, Huangwei Chen, Feiwei Qin, Zhu Zhu
arXiv:2607. 15280v1 Announce Type: new Abstract: Sequential diagnosis requires balancing diagnostic accuracy against resource costs through iterative information gathering.
By Shaoting Tan, Ning Liu, Yuntao Du, Shuyue Wei, Wu Shuai, Qian Li, Yanyu Xu, Wei Zhang, Lizhen Cui, Haitao Yuan
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.
By Wenliang Li, Rui Yan, Xu Zhang, Li Chen, Hongji Zhu, Jing Zhao, Junjun Li, Mengru Li, Wei Cao, Zihang Jiang, Wei Wei, Kun Zhang, Shaohua Kevin Zhou
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.
By Del Coburn, Scott Sanner, Dan Silver
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...
By Dongsheng Shi, Yue Li, Xin Yi, Linlin Wang
arXiv:2608.21948v1 Announce Type: new
Abstract: Complex clinical reasoning requires models to update diagnostic hypotheses as new evidence emerges and to coordinate different medical specialities und...
By Sike Xiang, Shuang Chen, Qian sun, Jia Cheng, Yusi Wei, Amir Atapour-Abarghouei
Disease diagnosis is a key application of large language model (LLM) agents in healthcare. However, existing medical agents are primarily trained and evaluated with static question-answering data, pro...
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...
By Sirry Chen, Fangke Chen, Xuanwen Ding, Wei Chen, Zhongyu Wei
MedCaseAgent is an end‑to‑end agentic reasoning system that generates publication‑quality rare disease case reports from unstructured clinical notes. It combines patient‑level data mining with iterative, tool‑integrated reasoning, dynamically querying medical literature and knowledge bases to maintain clinical fidelity and assess case novelty. The framework is modular, supports multiple backbone LLMs, and is accompanied by a new multimodal case‑report corpus (MedCase‑150K) and evaluation suite (MedCase‑Bench).
By Hongkang Chu, Yifan Shen, Tianjiao Yu, Zhuoqing Zhong, Jianguo Cao, Meihuan Huang, Xu Cao
Although Large Language Models (LLMs) have demonstrated potential in clinical documentation, generating the rigorous, evidence-based reasoning required for unprecedented, atypical, and rare disease ca...
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.
By Qi Peng, Jiatong Li, Sirui Huang, Yiyang Jiang, Kaisong Gong, Ronger Ding, Shijie Ye, Changmeng Zheng, Yi Cai, Xiaobo Yang, Jin Huang, Xiao-Yong Wei, Qing Li