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20232026
most citedFoundation Models in Radiology: What, How, When, Why and Why Not

90 citations · 240 across the 18 of their papers we have counts for

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Showing 2024Show all

7 papers · 1 filter

cs.AI2024★ 35 cited

Best Practices for Large Language Models in Radiology

Christian Bluethgen, Dave Van Veen, Cyril Zakka +7

At the heart of radiological practice is the challenge of integrating complex imaging data with clinical information to produce actionable insights. Nuanced application of language…

cs.LG2024★ 90 cited

Foundation Models in Radiology: What, How, When, Why and Why Not

Magdalini Paschali, Zhihong Chen, Louis Blankemeier +6

Recent advances in artificial intelligence have witnessed the emergence of large-scale deep learning models capable of interpreting and generating both textual and imaging data. Su…

eess.IV2024

Evaluating and Improving the Effectiveness of Synthetic Chest X-Rays for Medical Image Analysis

Eva Prakash, Jeya Maria Jose Valanarasu, Zhihong Chen +9

Purpose: To explore best-practice approaches for generating synthetic chest X-ray images and augmenting medical imaging datasets to optimize the performance of deep learning models…

cs.CV2024★ 1 cited

CheXalign: Preference fine-tuning in chest X-ray interpretation models without human feedback

Dennis Hein, Zhihong Chen, Sophie Ostmeier +8

Radiologists play a crucial role in translating medical images into actionable reports. However, the field faces staffing shortages and increasing workloads. While automated approa…

cs.CV2024★ 33 cited

Merlin: A Computed Tomography Vision-Language Foundation Model and Dataset

Louis Blankemeier, Ashwin Kumar, Joseph Paul Cohen +37

The large volume of abdominal computed tomography (CT) scans coupled with the shortage of radiologists have intensified the need for automated medical image analysis tools. Previou…

cs.CL2024★ 42 cited

GREEN: Generative Radiology Report Evaluation and Error Notation

Sophie Ostmeier, Justin Xu, Zhihong Chen +8

Evaluating radiology reports is a challenging problem as factual correctness is extremely important due to the need for accurate medical communication about medical images. Existin…

cs.CL2024★ 31 cited

A dataset and benchmark for hospital course summarization with adapted large language models

Asad Aali, Dave Van Veen, Yamin Ishraq Arefeen +9

Brief hospital course (BHC) summaries are clinical documents that summarize a patient's hospital stay. While large language models (LLMs) depict remarkable capabilities in automati…