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.
arXiv:2607. 17508v1 Announce Type: cross Abstract: We introduce Retrieval-Augmented Interpretable Learning (RAIL), a probabilistic meta-learning framework for zero-shot generation of task-specific interpretable models that synthesizes coefficient-space structure from natural-language task descriptions and a memory of previously learned task-specific predictors.
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.
arXiv:2607. 28788v1 Announce Type: new Abstract: Clinical diagnosis at hospital admission must be made rapidly from limited, incomplete evidence.
arXiv:2607. 20453v1 Announce Type: cross Abstract: Large language models show promise for clinical prediction, but zero-shot performance on specialized tasks is limited by incomplete domain knowledge, especially for smaller locally deployable models.
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.
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.
arXiv:2602. 19502v2 Announce Type: replace Abstract: Agentic AI systems are increasingly capable of autonomous data science workflows, yet clinical prediction tasks demand domain expertise that purely automated approaches struggle to provide.
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:2610.18529v2 Announce Type: replace Abstract: Jev has attracted considerable attention for its rapid, generalizable decision-making, yet its medical reliability remains underexplored. To addres...
arXiv:2606. 12006v1 Announce Type: cross Abstract: Predicting time-to-event outcomes such as mortality is a fundamental task in clinical decision-making, commonly addressed through survival analysis.
arXiv:2609.40361v1 Announce Type: cross Abstract: Multimodal large language models (MLLMs) are rapidly advancing clinical diagnosis, yet their adaptation pipelines remain anchored to accuracy-based o...
arXiv:2603.02790v2 Announce Type: replace Abstract: Foundation models are changing the way we develop medical artificial intelligence. By learning broadly generalizable features across diverse data m...