arXiv AI
Aug 3

Reasoning in Real World Clinical Care: Why Large Language Models Are Not Yet Safe for Autonomous Clinical Decision Support

arXiv:2607. 28677v1 Announce Type: new Abstract: LLM now pass medical licensing examinations and, in curated cases, can rival physicians at diagnostic reasoning.

By Shayndhan Sivanathan, Shravan Nageswaran, Mehdi Zadem, Ryaan Sultan, Nicolas von Mallinckrodt, Max Solovyev, Alexey Matyushkin, Sumon Sadhu, Gabriele C DeLuca, Sanjeeva Jeyaretna, James Hillis, Manoj Ramachandran, Prakash Jayakumar
arXiv Computation and Language
Sep 22

LLMs Anchor on Chief Complaint and Fail to Integrate Evidence in Sequential Clinical Triage

The study evaluates large language models (LLMs) on sequential emergency department triage, where acuity labels are predicted from progressively longer nurse‑patient conversations. Six LLMs were tested at five checkpoints on simulated and physician‑authored dialogues, showing a decline from moderate‑to‑substantial agreement on full records to only fair‑to‑moderate agreement at each checkpoint. The models consistently anchor on chief complaint exchanges and fail to integrate later evidence, yielding low agreement with clinicians (QWK 0.295 vs. 0.887‑0.929) and concentrating predictions on ESI‑2 and ESI‑3. whyItMatters":"The findings reveal that LLMs, despite strong offline performance, cannot reliably handle the sequential nature of real‑time triage, highlighting a critical gap for safe deployment in emergency settings."

By Dipankar Srirag, Haokai Zhao, Ashutosh Kumar, Eleanor Hopper, Michael Dalton, Quoc Dung Nguyen, Aditya Joshi, Salil S. Kanhere, Padmanesan Narasimhan
arXiv AI
Jul 7

Medi-Gemma: A Hybrid Clinical Decision Support System Integrating Deterministic EMR Analytics and Retrieval-Augmented Generation

arXiv:2607. 04907v1 Announce Type: new Abstract: Deploying Large Language Models (LLMs) in high-stakes clinical settings remains limited by structural hallucinations, weak deterministic reasoning over tabular patient data, and omissions in vector retrieval.

By Mohammed Saim Ahmed Quadri, Yunzhe Xue, Justin W. Ady, Usman Roshan
arXiv AI
Sep 11

Emergency Department Revisit Quality Review Screening: Exploring Human Decision-Making and Artificial Intelligence Support

The study examined how clinicians and GPT‑4 evaluate emergency department revisit pairs to determine if further assessment is needed. GPT‑4 over‑identified nearly all pairs as requiring follow‑up, while clinicians used clinical gravity and differential diagnosis factors. An algorithm using a knowledge graph populated by an LLM achieved high positive predictive value for identifying pairs that clinicians deemed warranting review.

By Jonathan A. Handler, Marlene I. Robles-Granda, Jacob E. Mefford, Jeremy S. McGarvey, Gregory S. Podolej, Colleen J. Klein, Matthew D. Dalstrom, William F. Bond
arXiv AI
Jul 10

A safety-oriented hypothetico-deductive framework for AI-assisted differential diagnosis

arXiv:2607. 08038v1 Announce Type: new Abstract: Diagnostic error is a major threat to patient safety, yet current large language model (LLM) systems often treat diagnosis as a one-shot prediction task, lacking safeguards against missed high-risk alternatives or rigorous verification of their reasoning.

By Fan Ma, Mauro Giuffr\`e, Donald Wright, Kent McCann, Mark Iscoe, Lingfei Qian, Mingyang Jiang, Chi Wing Ng, Na Hong, Huan He, Cathy Shyr, Qingyu Chen, Lee Schwamm, Lucila Ohno-Machado, Hua Xu