Multimodal generative AI for medical image interpretation

Vishwanatha M. Rao, Michael Hla, Michael Moor, Subathra Adithan, Stephen Kwak, Eric J. Topol () and Pranav Rajpurkar ()
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Vishwanatha M. Rao: Harvard Medical School
Michael Hla: Harvard Medical School
Michael Moor: Stanford University
Subathra Adithan: Harvard Medical School
Stephen Kwak: Johns Hopkins University
Eric J. Topol: Scripps Research
Pranav Rajpurkar: Harvard Medical School

Nature, 2025, vol. 639, issue 8056, 888-896

Abstract: 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 synthesize progress and challenges in developing AI systems for generation of medical reports from images. We focus extensively on radiology as a domain with enormous reporting needs and research efforts. In addition to analysing the strengths and applications of new models for medical report generation, we advocate for a novel paradigm to deploy GenMI in a manner that empowers clinicians and their patients. Initial research suggests that GenMI could one day match human expert performance in generating reports across disciplines, such as radiology, pathology and dermatology. However, formidable obstacles remain in validating model accuracy, ensuring transparency and eliciting nuanced impressions. If carefully implemented, GenMI could meaningfully assist clinicians in improving quality of care, enhancing medical education, reducing workloads, expanding specialty access and providing real-time expertise. Overall, we highlight opportunities alongside key challenges for developing multimodal generative AI that complements human experts for reliable medical report writing.

Date: 2025
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DOI: 10.1038/s41586-025-08675-y

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