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From the 1 of 26 linked papers with an AI index.

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20242026
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26 papers

cs.CV2026

Symbal: Detecting Systematic Misalignments in Model-Generated Captions

Maya Varma, Jean-Benoit Delbrouck, Sophie Ostmeier +2

The paper presents Symbal, a dual‑stage method that uses off‑the‑shelf foundation models to automatically detect systematic misalignments—recurring caption errors tied to specific…

cs.CV2026

BenchX: Benchmarking AI Models for Cancer Detection and Localization with Demographic and Protocol Biases

Qi Chen, Wenxuan Li, Pedro R. A. S. Bassi +14

Artificial intelligence (AI) has achieved remarkable success in medical imaging, but it is widely recognized that these models often perform inconsistently across real-world clinic…

cs.CV2026

CheXTemporal: A Dataset for Temporally-Grounded Reasoning in Chest Radiography

Eva Prakash, Yunhe Gao, Chong Wang +10

Chest radiograph interpretation requires temporal reasoning over prior and current studies, yet most vision-language models are trained on static image-report pairs and lack explic…

cs.CV2026

CheXthought: A global multimodal dataset of clinical chain-of-thought reasoning and visual attention for chest X-ray interpretation

Sonali Sharma, Jin Long, George Shih +7

Chest X-ray interpretation is one of the most frequently performed diagnostic tasks in medicine and a primary target for AI development, yet current vision-language models are prim…

cs.CV2026

A Reasoning-Enabled Vision-Language Foundation Model for Chest X-ray Interpretation

Yabin Zhang, Chong Wang, Yunhe Gao +19

Chest X-rays (CXRs) are among the most frequently performed imaging examinations worldwide, yet rising imaging volumes increase radiologist workload and the risk of diagnostic erro…

cs.CV2026

Activation Matters: Test-time Activated Negative Labels for OOD Detection with Vision-Language Models

Yabin Zhang, Maya Varma, Yunhe Gao +4

Out-of-distribution (OOD) detection aims to identify samples that deviate from in-distribution (ID). One popular pipeline addresses this by introducing negative labels distant from…