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20172026
most citedAdapted Large Language Models Can Outperform Medical Experts in Clinical Text Summarization

755 citations · 1.2k across the 42 of their papers we have counts for

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10 papers · 1 filter

cs.CV2026

Foundation model embeddings capture pre-diagnostic changes on screening mammograms

Kalina P. Slavkova, Eric Brattain, Aditya Gowd +6

Foundation model embeddings of screening mammograms may encode pre-diagnostic tissue change without task-specific adaptation. We tested whether embeddings move faster along a data-…

cs.CV2026

Symbal: Detecting Systematic Misalignments in Model-Generated Captions

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

Multimodal large language models (MLLMs) often introduce errors when generating image captions, resulting in misaligned image-text pairs. Our work focuses on a class of captioning…

eess.IV2026

Reconfigurable Radiology Labels Without Relabeling

Jean-Benoit Delbrouck, Dave Van Veen, Akash Pattnaik +4

Public chest-radiograph (CXR) datasets are typically released with small, fixed label schemas such as CheXpert-14. However, the underlying free-text reports describe far more findi…

cs.AI2026

RadAgent: A tool-using AI agent for stepwise interpretation of chest computed tomography

Mélanie Roschewitz, Kenneth Styppa, Yitian Tao +10

Vision-language models (VLM) have markedly advanced AI-driven interpretation and reporting of complex medical imaging, such as computed tomography (CT). Yet, existing methods large…

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.CL2026

Medmarks: A Comprehensive Open-Source LLM Benchmark Suite for Medical Tasks

Benjamin Warner, Ratna Sagari Grandhi, Max Kieffer +32

Evaluating large language models (LLMs) for medical applications remains challenging due to benchmark saturation, limited data accessibility, and insufficient coverage of relevant…