We trained a three-dimensional visual foundation model directly on 5.24 million routine clinical computed tomography (CT) and magnetic resonance imaging (MRI) image series so that it learned a shared representation of neuroanatomy and disease. The model demonstrated state-of-the-art <a href="https://healthylife7.com/the-first-at-home-test-for-infected-ticks-could-improve-lyme-disease-diagnosis/” title=”The First At-Home Test for Infected Ticks Could Improve Lyme Disease Diagnosis”>diagnosis, in contrast to foundation models that are trained on public Internet and medical data, and enabled preliminary report generation and triage in real health systems
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Subjects
- Image processing
- Machine learning
References
Radford, A. et al. Learning transferable visual models from natural language supervision. In Proc. 38th International Conference on Machine Learning 8748–8763 (2021). This paper established contrastive image-text pretraining (CLIP) as a foundation for modern vision-language models
Jiang, L. Y. et al. Health system-scale language models are all-purpose prediction engines. Nature619, 357–362 (2023). This study demonstrated that large-scale health system data can support broadly useful clinical prediction models
Assran, M. et al. Self-supervised learning from images with a joint-embedding predictive architecture. In Proc. IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 15619–15629 (2023). This paper introduced I-JEPA, the self-supervised representation learning method adapted here for volumetric medical imaging
Lyu, Y. et al. Learning neuroimaging models from health system-scale data. Nat. Biomed. Eng.https://doi.org/10.1038/s41551-025-01608-0 (2026). This paper showed strong performance with neuroimaging learning from MRI–report pairs at health system scale
Moor, M. et al. Foundation models for generalist medical artificial intelligence. Nature616, 259–265 (2023). This perspective outlines the rationale for generalist medical AI models that learn across heterogeneous clinical data modalities and support multiple downstream tasks
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This is a summary of: Kondepudi, A. et al. Health system learning enables generalist neuroimaging models. Nat. Med. https://doi.org/10.1038/s41591-026-04497-1 (2026)
A.K. and T.H. used ChatGPT to help prepare their contribution to this Research Briefing
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Learning from routine health system data builds better neuroimaging AI models.
Nat Med (2026). https://doi.org/10.1038/s41591-026-04567-4
Published:31 July 2026
Version of record:31 July 2026
DOI
:https://doi.org/10.1038/s41591-026-04567-4
