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:2512. 01241v4 Announce Type: replace-cross Abstract: Large language models (LLMs) and medical AI tools are routinely used by physicians and patients for medical advice, yet their clinical safety profiles remain poorly characterized.
By David Wu, Fateme Nateghi Haredasht, Saloni Kumar Maharaj, Priyank Jain, Jessica Tran, Matthew Gwiazdon, Arjun Rustagi, Jenelle Jindal, Jacob M. Koshy, Vinay Kadiyala, Anup Agarwal, Bassman Tappuni, Brianna French, Sirus Jesudasen, Christopher V. Cosgriff, Rebanta Chakraborty, Jillian Caldwell, Susan Ziolkowski, David J. Iberri, Robert Diep, Rahul S. Dalal, Kira L. Newman, Kristin Galetta, J. Carl Pallais, Nancy Wei, Kathleen M. Buchheit, David I. Hong, Vartan Pahalyants, Ernest Y. Lee, Allen Shih, Tamara B. Kaplan, Vishnu Ravi, Sarita Khemani, Thomas A. Buckley, April S. Liang, Daniel Shirvani, Advait Patil, Nicholas Marshall, Kanav Chopra, Joel Koh, Adi Badhwar, Anastasia Perez, Austin J. Schoeffler, Mahbuba Tusty, Chase M. Walton, Liam G. McCoy, David J. H. Wu, Yingjie Weng, Sumant Ranji, Kevin Schulman, Nigam H. Shah, Jason Hom, Arnold Milstein, Arjun K. Manrai, Adam Rodman, Jonathan H. Chen, Ethan Goh
arXiv:2509. 02594v3 Announce Type: replace-cross Abstract: Evaluating large language models (LLMs) on their ability to generate high-quality, accurate, situationally aware answers to clinical questions requires going beyond conventional benchmarks to assess how these systems behave in complex, high-stakes clinical scenarios.
By Sandhanakrishnan Ravichandran, Shivesh Kumar, Rogerio Corga Da Silva, Miguel Romano, Reinhard Berkels, Michiel van der Heijden, Olivier Fail, Valentine Emmanuel Gnanapragasam
arXiv:2607. 08257v1 Announce Type: new Abstract: Large language models (LLMs) have shown strong performance on isolated psychiatric tasks, including dialogue, diagnosis, and treatment planning, yet existing benchmarks rarely simulate complete psychiatric clinical encounters.
By Yuming Yang, Xiao Sun, Yuanwei Zou, Zhengxiao Wu, Yun Chen, Jiang Zhong, Haoyang Zeng, Jingwang Huang, Kaiwen Wei
arXiv:2603. 14158v2 Announce Type: replace-cross Abstract: Large language models (LLMs) are entering clinical workflows, yet evaluations rarely assess how clinician reasoning shapes model behavior during clinical interactions.
By Ivan Lopez, Selin S. Everett, Bryan J. Bunning, April S. Liang, Dong Han Yao, Shivam C. Vedak, Kameron C. Black, Sophie Ostmeier, Stephen P. Ma, Emily Alsentzer, Jonathan H. Chen, Akshay S. Chaudhari, Eric Horvitz
arXiv:2608. 06110v1 Announce Type: new Abstract: This paper presents ECHO (Enhanced Care \& Health Observer), a locally-deployable conversational health assistant for long-term chronic care management.
By Abdulkadir K\"ul\c{c}e, Alihan Esen, Ca\u{g}la Fikir, Berke Kurt, Kuzey Arar, G\"okhan Ercan, Faik Boray Tek