The study evaluates large language models (LLMs) on unprocessed electronic medical record data for clinical registry abstraction, focusing on the American College of Cardiology National Cardiovascular Data Registry. In a pilot at one academic center, the LLM identified candidate data sources for each registry question, which abstractors used to define question‑specific document sets. In a subsequent validation at a second center, the LLM answered 157 registry questions with an overall mean accuracy of 91.5%, but accuracy dropped from 96% for simple medication or event flag questions to 62% for event timing questions, reflecting increasing ambiguity and required clinical reasoning.
By James Matheson, Betsy Castillo, Andrew Y. Shin, David Scheinker
arXiv:2609.13824v1 Announce Type: new
Abstract: Large language models (LLMs) are increasingly used to evaluate the responses of other language models. This approach, known as LLM-as-a-Judge, is faste...
By Aakash Kumar Tiwari
The paper introduces PrecepTron, a 32‑billion‑parameter language model fine‑tuned with low‑rank adaptation to evaluate clinical reasoning in large language models (LLMs) at a physician level. It also releases GRAND‑ROUNDS, a benchmark of 9,217 scored responses from 160 clinicians across seven studies. Using PrecepTron, the authors replicate key findings from major medical AI studies and explore new questions about LLM diagnostic accuracy, demonstrating that fine‑tuned models can provide consistent, scalable physician‑level scoring.
By Thomas A. Buckley, Zahir Kanjee, Peter G. Brodeur, Byron Crowe, Anthony M. Pettinato, Aashna P. Shah, Adrian D. Haimovich, Liam G. McCoy, Daniel Restrepo, Jason A. Freed, Ethan Goh, Jonathan H. Chen, Laura Zwaan, Katherine E. Goodman, Daniel J. Morgan, Raja-Elie E. Abdulnour, Adam Rodman, Arjun K. Manrai
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:2606. 03198v1 Announce Type: cross Abstract: Clinical AI evaluation increasingly delegates scoring to large language models (LLMs) acting as AI raters, yet their scoring behavior across evaluation conditions has not been quantitatively characterized.
By Sangwon Baek, Kyu Yeon Hur, Kyunga Kim
AcuityBench is a new benchmark that tests whether language models can correctly identify the urgency of medical care needed from user presentations. It unifies five public datasets—user conversations, online forum posts, clinical vignettes, and patient portal messages—under a shared four-level acuity framework, providing 914 cases for evaluation. The benchmark supports both explicit four-way classification and free-form conversational responses, revealing that models vary widely in accuracy and that conversational formats reduce over-triage but increase under-triage, especially for high-acuity cases.
By Robin Linzmayer (Department of Computer Science, Columbia University, Department of Biomedical Informatics, Columbia University), Georgianna Lin (Department of Biomedical Informatics, Columbia University), Di Coneybeare (Department of Emergency Medicine, Columbia University Irving Medical Center), Jason Chu (Department of Emergency Medicine, Columbia University Irving Medical Center), Trudi Cloyd (Department of Emergency Medicine, Columbia University Irving Medical Center), Manish Garg (Department of Emergency Medicine, Columbia University Irving Medical Center), Miles Gordon (Department of Emergency Medicine, Columbia University Irving Medical Center), Elizabeth Hartofilis (Department of Emergency Medicine, Columbia University Irving Medical Center), Benjamin Hong (Department of Emergency Medicine, Columbia University Irving Medical Center), Ashraf Hussain (Department of Emergency Medicine, Columbia University Irving Medical Center), Eugene Y. Kim (Department of Emergency Medicine, Columbia University Irving Medical Center), Oluchi Iheagwara King (Department of Emergency Medicine, Columbia University Irving Medical Center), Ross McCormack (Department of Emergency Medicine, Columbia University Irving Medical Center), Erica Olsen (Department of Emergency Medicine, Columbia University Irving Medical Center), John K. Riggins Jr (Department of Emergency Medicine, Columbia University Irving Medical Center), Mustafa N. Rasheed (Department of Emergency Medicine, Columbia University Irving Medical Center), Dana L. Sacco (Department of Emergency Medicine, Columbia University Irving Medical Center), Vinay Saggar (Department of Emergency Medicine, Columbia University Irving Medical Center), Osman R. Sayan (Department of Emergency Medicine, Columbia University Irving Medical Center), Amit Shembekar (Department of Emergency Medicine, Columbia University Irving Medical Center), Janice Shin-Kim (Department of Emergency Medicine, Columbia University Irving Medical Center), Wendy W. Sun (Department of Emergency Medicine, Columbia University Irving Medical Center), Bernard P. Chang (Department of Emergency Medicine, Columbia University Irving Medical Center), David Kessler (Department of Emergency Medicine, Columbia University Irving Medical Center), No\'emie Elhadad (Department of Computer Science, Columbia University, Department of Biomedical Informatics, Columbia University)
arXiv:2607. 18828v1 Announce Type: new Abstract: Readiness stress-testing of medical AI has focused on closed-ended and multimodal benchmarks.
By Koyar Afrasyab
arXiv:2609.37788v1 Announce Type: cross
Abstract: Rubrics support the structured evaluation of language models. We propose a rubric for assessing expressed clinical reasoning in model responses, draw...
By Zhangshu Joshua Jiang, Zina Ibrahim, James T. Teo
arXiv:2606. 28960v1 Announce Type: new Abstract: Physicians now pose millions of clinical questions to AI tools each week, yet these tools are evaluated largely on hypothetical or exam-style questions, not those actually asked in practice.
By Jean Feng, Vishal Patel, Patrick Heagerty, Yifan Mai, Venkatesh Sivaraman, Patrick Vossler, Jialin Ouyang, Anupam B. Jena
arXiv:2607. 18086v1 Announce Type: new Abstract: Background: LLM judges increasingly score whether clinical language models give overconfident answers under incomplete evidence, yet whether a measured "safety gain" reflects real behavior change or the judge's calibration is unresolved.
By Koyar Afrasyab
arXiv:2604. 14892v3 Announce Type: replace-cross Abstract: Evaluating medical AI systems using expert clinician panels is costly and slow, motivating the use of large language models (LLMs) as alternative adjudicators.
By Amy Rouillard, Sitwala Mundia, Linda Camara, Ziyaad Dangor, Michael Cameron Gramanie, Ismail Kalla, Shabir A. Madhi, Kajal Morar, Marlvin T. Ncube, Haroon Saloojee, Bruce A. Bassett
The paper introduces the first benchmark for evaluating confidence estimation in large language models during multi‑turn medical consultations, combining three types of medical data and an information sufficiency gradient to capture how confidence and correctness evolve as evidence accumulates. Experiments with 27 methods reveal that token‑level and consistency‑level confidence approaches are limited by medical data, and that medical reasoning must be judged on both diagnostic accuracy and information completeness. Building on these findings, the authors propose MedConf, a retrieval‑augmented, linguistically grounded self‑assessment framework that aligns patient information with supporting, missing, and contradictory relations, producing interpretable confidence estimates that outperform existing methods across multiple datasets and LLMs.
By Zhiyao Ren, Yibing Zhan, Siyuan Liang, Guozheng Ma, Baosheng Yu, Dacheng Tao