arXiv AI By Jeongmoon Won, Seungwon Kook, Yohan Jo

Think Like a Doctor: Conversational Diagnosis through the Exploration of Diagnostic Knowledge Graphs

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arXiv Computation and Language
Sep 1

MedConceal: A Benchmark for Clinical Hidden-Concern Reasoning Under Partial Observability

MedConceal is a new benchmark for evaluating medical dialogue systems on hidden‑concern reasoning under partial observability. It features 300 curated cases and 600 clinician‑LLM interactions, using an interactive patient simulator that hides latent concerns and tracks their revelation and resolution through theory‑grounded communication signals. The benchmark assesses both confirmation (surfacing hidden concerns) and intervention (addressing the primary concern), revealing that current models excel on different metrics while human clinicians still outperform them on intervention success.

By Yikun Han, Joey Chan, Jingyuan Chen, Mengting Ai, Simo Du, Yue Guo
arXiv AI
Sep 28

ConsultMind:Towards Automated Diagnostic Consultation via Uncertainty-Aware Reasoning

ConsultMind is an uncertainty‑aware framework that automates diagnostic consultation by updating disorder posteriors after each patient response and using posterior uncertainty to guide inquiry and diagnosis. It builds on AutoDisym, a pipeline that constructs a Disorder–Symptom Bayesian Network (DSBN) from diagnostic knowledge and clinical narratives. Across psychiatry, respiratory medicine, fever clinics, and public datasets, AutoDisym produces high‑quality DSBNs and ConsultMind improves diagnostic accuracy and explanation quality, achieving up to 22.15‑point gains in Top‑1 accuracy and 37.89‑point gains in Top‑3 accuracy.

By Xiao Sun, Yuming Yang, Yun Chen, Jiang Zhong, Junnan Zhu, Xinyi Jiang, Haoyang Zeng, Ruirui Chen, Yining Wang, Xinyu Zhou, Rong Tang, Kaiwen Wei