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
The study evaluated the accuracy and calibration of several Decision and System One models on rheumatology specialty‑training exam questions and diagnostic vignettes. On the RheumaMIR exam dataset, Jev achieved 93.0% accuracy in Spanish and 91.9% in English, while GPT‑6 Sol reached 99.4%. On the RheumBench diagnostic vignettes, GPT‑6 Sol again led with 76.9% accuracy, with Jev and other System One models scoring significantly lower (50.0%–59.6%).
By Alfredo Madrid-Garc\'ia, Diego Benavent, Fabian Lechner, Phillip Kremer, Johannes Knitza, Beatriz Merino-Barbancho
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
arXiv:2609.13238v1 Announce Type: new
Abstract: Maxillofacial report generation from cone beam computed tomography is scored here by a composite objective placing 80% of its weight on a large languag...
By Ajo Babu George, Govind Arun, Sidharth N Krishna, Uma Ranjan
arXiv:2610.32478v1 Announce Type: new
Abstract: Vision-language models used for decisions in histopathology rarely return calibrated probabilities over the candidates a user cares about. Decision-nat...
By Cristian Tommasino, Antonio M. Rinaldi
MedJev is an open System One model that answers medical questions in a single forward pass without generating text, enabling fast, local clinical decision support. Across ten medical benchmarks, MedJev achieves competitive accuracies—72.5 % on MedQA, 75.4 % on USMLE, and 80.1 % on NEJM—while maintaining well‑calibrated confidence estimates. On patient‑level tasks, it attains an AUROC of 0.926 for in‑hospital mortality, surpassing XGBoost, and introduces a lightweight two‑parameter recalibration method that reduces miscalibration from 0.286 to 0.007 using only 50–200 labeled examples.
By Simon A. Lee, Jiahang Sha, Moonseong Jeong, Yunbei Pan, Jeffrey N. Chiang
The study evaluates four open language models (8–27 billion parameters) on the Spanish MIR residency exam, covering 1,552 questions across eight editions. The largest models could rank within the top 600–3,000 candidates and had 41–43 specialties still open, yet abstaining never improved performance because even low‑confidence answers were more often correct than the 25% break‑even threshold. Medical fine‑tuning lowered overconfidence but also reduced accuracy, did not improve the correlation between confidence and correctness, and removing or swapping the provided images did not affect accuracy.
By Miguel Fernandez-de-Retana, Unai Zulaika, Ruben Sanchez-Corcuera, Aritz Bilbao-Jayo, Aitor Almeida
arXiv:2606. 15910v2 Announce Type: replace Abstract: A vision-language model can answer a question about a chest radiograph or a pathology slide fluently and confidently while barely using the image, relying instead on language priors.
By Reza Khanmohammadi, Kundan Thind, Mohammad M. Ghassemi
The paper introduces Clinical System One (CS1), a 4‑billion‑parameter decision model that rapidly produces typed judgments from clinical records. Trained on 12.4 million Q&A pairs from 3.6 million patients, CS1 can answer multiple questions in a single forward pass, providing probability estimates for uncertain cases. In evaluations on 963,408 held‑out patients and 21 external datasets, CS1 outperformed larger open models and, when paired with a language‑model agent, efficiently identified trial‑eligible patients with far fewer tokens.
By Andrew Zhang, Trinh T. L. Vuong, Sophia J. Wagner, Dongmin Bang, Tong Ding, Yuchang Su, Mary Cipperman, Christina S. Chen, Judith Lefkes, Ramin Kahidi, Ming Y. Lu, Caiwei Tian, Tianhao Luo, Jingnan Ma, Rowland Pettit, Long Phi Le, Faisal Mahmood
The study evaluates Jev 1.13, a non‑generative model that selects from predefined answer options, on four medical benchmarks: MetaMedQA, PubMedQA, DiagnosisArena‑MCQ, and the NEJM Case Challenges. Jev’s top‑1 accuracy matches GPT‑6 Sol with medium reasoning on PubMedQA but falls behind on MetaMedQA, DiagnosisArena‑MCQ, and NEJM cases. While Jev shows strong calibration on MetaMedQA and is fast and inexpensive, its performance on examination and complex diagnostic tasks is substantially lower, indicating the need for task‑specific validation before clinical deployment.
By Alfredo Madrid-Garc\'ia, Beatriz Merino-Barbancho
arXiv:2609.34024v1 Announce Type: cross
Abstract: Jev is a non-generative "System One" model that assigns probabilities to predefined answer options and cannot answer outside them. Its accuracy and c...
By Alfredo Madrid-Garc\'ia, Beatriz Merino-Barbancho
OmniMed-Jev is a new medical multimodal model that represents each decision as a Choice, Noul, or Score over a runtime-supplied candidate set, returning a full probability distribution for each decision. By unifying diverse imaging modalities and prediction tasks into a single candidate-conditioned probability model, it makes heterogeneous outputs comparable probabilities rather than task-specific strings. In controlled comparisons against a generative baseline, OmniMed-Jev’s reported probabilities align more closely with observed correctness, reducing calibration error by up to an order of magnitude and reliability error by up to two, while maintaining comparable point-prediction performance.
By Luyao Tang, Cheng Chen