Peer-Reviewed Publication
Nature2025;639(8056):888-896.March 1, 2025Journal Article
Multimodal generative AI for medical image interpretation.
Vishwanatha M Rao1,2, Michael Hla1,3, Michael Moor4,5, Subathra Adithan1,6, Stephen Kwak7, Eric J Topol8, Pranav Rajpurkar9
1Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA.
2Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
3Department of Computer Science, Harvard College, Cambridge, MA, USA.
4Department of Computer Science, Stanford University, Stanford, CA, USA.
5Department of Biosystems Science and Engineering, ETH Zurich, Zurich, Switzerland.
6Department of Radiodiagnosis, Jawaharlal Institute of Postgraduate Medical Education and Research, Puducherry, India.
7Department of Radiology, Johns Hopkins University, Baltimore, MD, USA.
8Scripps Research, La Jolla, CA, USA. etopol@scripps.edu.
9Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA. pranav_rajpurkar@hms.harvard.edu.
Abstract
Accurately interpreting medical images and generating insightful narrative reports is indispensable for patient care but places heavy burdens on clinical experts. Advances in artificial intelligence (AI), especially in an area that we refer to as multimodal generative medical image interpretation (GenMI), create opportunities to automate parts of this complex process. In this Perspective, we synth…
Create a free account to keep reading
Free members get 10 full research views every month across publications, clinical trials, FDA clearances, adverse events, and NIH grants. No credit card required.
Want unlimited research access? See Pro plans