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
2d ago

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 Computation and Language
Aug 27

MTDiag: A Multi-Turn Diagnostic Dataset Towards Clinically Meaningful LLM Evaluation

MTDiag is a newly released multi-turn diagnostic dialogue dataset designed to evaluate large language models (LLMs) in clinically meaningful ways. It is built from DDXPlus, MIMIC-IV, and AJCR case reports, covering both common emergency department presentations and rare conditions, and normalizes cases into a canonical schema using UMLS concept identifiers and ICD-10 codes. The dataset includes a UserLM‑8B utterance‑generation pipeline and physician‑validated natural‑language utterances, and introduces clinical knowledge‑grounded metrics that go beyond simple diagnostic accuracy for multi‑turn differential diagnosis tasks.

By Pia Chouayfati, Alexander M. Fichtl, Miriam Ansch\"utz, George Doumat, Georg Groh