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20242026
most citedFoundation Models in Radiology: What, How, When, Why and Why Not

90 citations · 103 across the 16 of their papers we have counts for

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Showing 2025Show all

7 papers · 1 filter

cs.CV2025

TRoVe: Discovering Error-Inducing Static Feature Biases in Temporal Vision-Language Models

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

Vision-language models (VLMs) have made great strides in addressing temporal understanding tasks, which involve characterizing visual changes across a sequence of images. However,…

cs.CL2025

Process Reward Models for Sentence-Level Verification of LVLM Radiology Reports

Alois Thomas, Maya Varma, Jean-Benoit Delbrouck +1

Automating radiology report generation with Large Vision-Language Models (LVLMs) holds great potential, yet these models often produce clinically critical hallucinations, posing se…

cs.CV2025

From Detection to Mitigation: Addressing Bias in Deep Learning Models for Chest X-Ray Diagnosis

Clemence Mottez, Louisa Fay, Maya Varma +2

Deep learning models have shown promise in improving diagnostic accuracy from chest X-rays, but they also risk perpetuating healthcare disparities when performance varies across de…

cs.CL20251 cited

MedVAL: Toward Expert-Level Medical Text Validation with Language Models

Asad Aali, Vasiliki Bikia, Maya Varma +24

With the growing use of language models (LMs) in clinical environments, there is an immediate need to evaluate the accuracy and safety of LM-generated medical text. Currently, such…

cs.CL2025

Automated Structured Radiology Report Generation

Jean-Benoit Delbrouck, Justin Xu, Johannes Moll +11

Automated radiology report generation from chest X-ray (CXR) images has the potential to improve clinical efficiency and reduce radiologists' workload. However, most datasets, incl…

cs.LG2025

SMMILE: An Expert-Driven Benchmark for Multimodal Medical In-Context Learning

Melanie Rieff, Maya Varma, Ossian Rabow +9

Multimodal in-context learning (ICL) remains underexplored despite significant potential for domains such as medicine. Clinicians routinely encounter diverse, specialized tasks req…