Hugging Face Trending Papers
The Value of System 1 Models in Medicine
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arXiv Machine Learning
Oct 23
The Value of System 1 Models in Medicine
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 AI
2d ago
Decision and System One models in rheumatology: accuracy and calibration on specialty-training examination questions and diagnostic case vignettes
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%).
arXiv AI
Oct 15
ClinEdge: An Expert-Graded Benchmark of Real Patient Edge Cases Where Frontier Multimodal Models Fail
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...
arXiv Computation and Language
Sep 15
Clinical Reasoning Under a Partially Observed Objective in Cone Beam CT Report Generation
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...
arXiv Computer Vision
Oct 26
PathJev: Decision-Native Vision-Language Modelling for Histopathology
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...
arXiv AI
5d ago
MedJev: Evaluating Zero-Shot System-One Decision Making in Medicine
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.