The study evaluates four zero‑shot System 1 models on 86 public clinical tasks across 14 domains and 20 languages, measuring performance via AUROC. The best models achieved high AUROC scores—0.94 for medical knowledge, 0.93 for text understanding, 0.83 for clinical judgement from text, 0.89 for image perception, 0.84 for 2D image findings, and 0.69 for volumetric findings—yet performed worse on imaging tasks and varied significantly with language. System 1 models were fast (0.34–0.44 s) and energy‑efficient (24–81 J) but lagged behind clinicians on triage and were outperformed by generative models in some metrics.
By Xiufeng Yang, Konstantinos Vrettos, Zeineb Ben Chaaben, Fabian Drexel, Jannik L\"ubberstedt, Johannes Moll, Anirudh Narayanan, Frederike Stumpf, Cosmin I. Bercea, Jiawen Wang, Evangelia E. Vassalou, Krischan Braitsch, Sebastian Ziegelmayer, Markus Graf, Marcus R. Makowski, Lisa Adams, Nassir Navab, Michail E. Klontzas, Keno Bressem
Many clinical decisions are a choice among a few fixed options, taken quickly and in large numbers. Such decisions also need an uncertainty estimate to know when to defer to a clinician. Generative la...
arXiv:2610.15239v1 Announce Type: new
Abstract: Most benchmarks for large language models in medicine are built from licensing examinations and theoretical questions. The few that draw on real clinic...
By Anurag Sharma, Aditya Sharma, Sapna Baalwan
The study introduces MedQADE, a German open‑response clinical benchmark with 3,800 question‑answer pairs and physician reference annotations. It evaluates large language models (LLMs) as judges, finding that while some LLMs (e.g., Gemini 3 Flash) achieve physician‑level agreement on correctness, they exhibit self‑bias and low abstention rates. Physicians showed moderate agreement on correctness but limited agreement on difficulty, and their abstention increased with perceived difficulty.
By William Philipp, Finn Fassbender, Daniel Fister, Thorsten Langer, Martje G. Pauly, Rebecca Herzog, Markus A. Hobert, Theresa Paulus, Alexander Baumann, Chi Wang Ip, Lukas L. Goede, Johanna Reimer, Sebastian L\"ons, Ronald B\"ock, Sebastian Fudickar
arXiv:2608.01017v2 Announce Type: replace-cross
Abstract: Large language models can answer a medical question correctly and still abandon that answer when a user pushes back. We study this failure as...
By Kaike Ping, Buse \c{C}ar{\i}k, Caleb Wohn, Xiaohan Ding, Tongshuai Wang, Eugenia Rho
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