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20232025
most citedMultimodal Healthcare AI: Identifying and Designing Clinically Relevant Vision-Language Applications for Radiology

78 citations · 279 across the 19 of their papers we have counts for

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Showing 2024Show all

7 papers · 1 filter

eess.IV2024★ 4 cited

MedImageInsight: An Open-Source Embedding Model for General Domain Medical Imaging

Noel C. F. Codella, Ying Jin, Shrey Jain +28

In this work, we present MedImageInsight, an open-source medical imaging embedding model. MedImageInsight is trained on medical images with associated text and labels across a dive…

eess.IV2024

Scalable Drift Monitoring in Medical Imaging AI

Jameson Merkow, Felix J. Dorfner, Xiyu Yang +6

The integration of artificial intelligence (AI) into medical imaging has advanced clinical diagnostics but poses challenges in managing model drift and ensuring long-term reliabili…

cs.CL2024★ 16 cited

MAIRA-2: Grounded Radiology Report Generation

Shruthi Bannur, Kenza Bouzid, Daniel C. Castro +18

Radiology reporting is a complex task requiring detailed medical image understanding and precise language generation, for which generative multimodal models offer a promising solut…

cs.HC2024★ 1 cited

Challenges for Responsible AI Design and Workflow Integration in Healthcare: A Case Study of Automatic Feeding Tube Qualification in Radiology

Anja Thieme, Abhijith Rajamohan, Benjamin Cooper +22

Nasogastric tubes (NGTs) are feeding tubes that are inserted through the nose into the stomach to deliver nutrition or medication. If not placed correctly, they can cause serious h…

cs.CL2024★ 40 cited

Towards a clinically accessible radiology foundation model: open-access and lightweight, with automated evaluation

Juan Manuel Zambrano Chaves, Shih-Cheng Huang, Yanbo Xu +24

The scaling laws and extraordinary performance of large foundation models motivate the development and utilization of such models in biomedicine. However, despite early promising r…

cs.HC2024★ 78 cited

Multimodal Healthcare AI: Identifying and Designing Clinically Relevant Vision-Language Applications for Radiology

Nur Yildirim, Hannah Richardson, Maria T. Wetscherek +18

Recent advances in AI combine large language models (LLMs) with vision encoders that bring forward unprecedented technical capabilities to leverage for a wide range of healthcare a…

cs.CV2024★ 69 cited

Exploring scalable medical image encoders beyond text supervision

Fernando Pérez-García, Harshita Sharma, Sam Bond-Taylor +12

Language-supervised pre-training has proven to be a valuable method for extracting semantically meaningful features from images, serving as a foundational element in multimodal sys…