TY - JOUR
T1 - Multimodal generative AI for interpreting 3D medical images and videos
AU - Lee, Jung Oh
AU - Zhou, Hong Yu
AU - Berzin, Tyler M.
AU - Sodickson, Daniel K.
AU - Rajpurkar, Pranav
N1 - Publisher Copyright:
© The Author(s) 2025.
PY - 2025/12
Y1 - 2025/12
N2 - 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.
AB - 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.
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U2 - 10.1038/s41746-025-01649-4
DO - 10.1038/s41746-025-01649-4
M3 - Review article
AN - SCOPUS:105005167953
SN - 2398-6352
VL - 8
JO - npj Digital Medicine
JF - npj Digital Medicine
IS - 1
M1 - 273
ER -