28 citations · 43 across the 6 of their papers we have counts for
7 papers
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…
The Impact of AI Assistance on Radiology Reporting: A Pilot Study Using Simulated AI Draft Reports
Julián N. Acosta, Siddhant Dogra, Subathra Adithan +4
Radiologists face increasing workload pressures amid growing imaging volumes, creating risks of burnout and delayed reporting times. While artificial intelligence (AI) based automa…
ReXErr: Synthesizing Clinically Meaningful Errors in Diagnostic Radiology Reports
Vishwanatha M. Rao, Serena Zhang, Julian N. Acosta +2
Accurately interpreting medical images and writing radiology reports is a critical but challenging task in healthcare. Both human-written and AI-generated reports can contain error…
Direct Preference Optimization for Suppressing Hallucinated Prior Exams in Radiology Report Generation
Oishi Banerjee, Hong-Yu Zhou, Subathra Adithan +3
Recent advances in generative vision-language models (VLMs) have exciting potential implications for AI in radiology, yet VLMs are also known to produce hallucinations, nonsensical…
MedVersa: A Generalist Foundation Model for Medical Image Interpretation
Hong-Yu Zhou, Julián Nicolás Acosta, Subathra Adithan +3
Current medical AI systems are often limited to narrow applications, hindering widespread adoption. We present MedVersa, a generalist foundation model trained on tens of millions o…
Style-Aware Radiology Report Generation with RadGraph and Few-Shot Prompting
Benjamin Yan, Ruochen Liu, David E. Kuo +8
Automatically generated reports from medical images promise to improve the workflow of radiologists. Existing methods consider an image-to-report modeling task by directly generati…
Multimodal Image-Text Matching Improves Retrieval-based Chest X-Ray Report Generation
Jaehwan Jeong, Katherine Tian, Andrew Li +7
Automated generation of clinically accurate radiology reports can improve patient care. Previous report generation methods that rely on image captioning models often generate incoh…