[2602.01297] RE-MCDF: Closed-Loop Multi-Expert LLM Reasoning for Knowledge-Grounded Clinical Diagnosis

Computer Science > Artificial Intelligence arXiv:2602.01297 (cs) [Submitted on 1 Feb 2026 (v1), last revised 23 Mar 2026 (this version, v2)] Title:RE-MCDF: Closed-Loop Multi-Expert LLM Reasoning for Knowledge-Grounded Clinical Diagnosis Authors:Shaowei Shen, Xiaohong Yang, Jie Yang, Lianfen Huang, Yongcai Zhang, Yang Zou, Seyyedali Hosseinalipour View a PDF of the paper titled RE-MCDF: Closed-Loop Multi-Expert LLM Reasoning for Knowledge-Grounded Clinical Diagnosis, by Shaowei Shen and 6 other authors View PDF HTML (experimental) Abstract:Electronic medical records (EMRs), particularly in neurology, are inherently heterogeneous, sparse, and noisy, which poses significant challenges for large language models (LLMs) in clinical diagnosis. In such settings, single-agent systems are vulnerable to self-reinforcing errors, as their predictions lack independent validation and can drift toward spurious conclusions. Although recent multi-agent frameworks attempt to mitigate this issue through collaborative reasoning, their interactions are often shallow and loosely structured, failing to reflect the rigorous, evidence-driven processes used by clinical experts. More fundamentally, existing approaches largely ignore the rich logical dependencies among diseases, such as mutual exclusivity, pathological compatibility, and diagnostic confusion. This limitation prevents them from ruling out clinically implausible hypotheses, even when sufficient evidence is available. To overcome these, ...