[2509.20067] MACD: Multi-Agent Clinical Diagnosis with Self-Learned Knowledge for LLM

Computer Science > Artificial Intelligence arXiv:2509.20067 (cs) [Submitted on 24 Sep 2025 (v1), last revised 27 Feb 2026 (this version, v4)] Title:MACD: Multi-Agent Clinical Diagnosis with Self-Learned Knowledge for LLM Authors: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 View a PDF of the paper titled MACD: Multi-Agent Clinical Diagnosis with Self-Learned Knowledge for LLM, by Wenliang Li and 12 other authors View PDF Abstract:Large language models (LLMs) have demonstrated notable potential in medical applications, yet they face substantial challenges in handling complex real-world clinical diagnoses using conventional prompting methods. Current prompt engineering and multi-agent approaches typically optimize isolated inferences, neglecting the accumulation of reusable clinical experience. To address this, this study proposes a novel Multi-Agent Clinical Diagnosis (MACD) framework, which allows LLMs to self-learn clinical knowledge via a multi-agent pipeline that summarizes, refines, and applies diagnostic insights. It mirrors how physicians develop expertise through experience, enabling more focused and accurate diagnosis on key disease-specific cues. We further extend it to a MACD-human collaborative workflow, where multiple LLM-based diagnostician agents engage in iterative consultations, supported by an evaluator agent and human oversight for cases where agreement i...