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Multimodal generative AI for interpreting 3D medical images and videos
Jung-Oh Lee (Seoul National University, Seoul National University Hospital), Hong-Yu Zhou (Harvard University), Tyler M. Berzin (Beth Israel Deaconess Medical Center, Harvard University), Daniel K. Sodickson (Center for Advanced Imaging Innovation and Research, New York University), Pranav Rajpurkar (Harvard University)
Résumé : This perspective proposes adapting video-text generative AI to 3D medical imaging (CT/MRI) and medical videos (endoscopy/laparoscopy) by treating 3D images as videos. The approach leverages modern video models to analyze multiple sequences simultaneously and provide real-time AI assistance during procedures. The paper examines medical imaging's unique characteristics (synergistic information, metadata, and world model), outlines applications in automated reporting, case retrieval, and education, and addresses challenges of limited datasets, benchmarks, and specialized training.