ArticleNature biomedical engineering2025
A data-efficient strategy for building high-performing medical foundation models.
Article in Nature biomedical engineering, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
13 citing papers in PubMed.
- From generalization to precision: A large domain-specific pretrained model for specialized medical tasks.Cell reports. Medicine · 2026Article
- Review
- Fully automated system predicts osteoporotic vertebral fracture across institutions using lumbar MRI paraspinal muscle signatures.NPJ digital medicine · 2026Article
- AUBADE-syn: a novel deep learning ensemble method for glaucoma detection using synthetic fundus images on imbalanced datasets.NPJ digital medicine · 2026Article
- Foundation models in healthcare: a comprehensive review from technical advances to clinical translation.Journal of translational medicine · 2026Review
- Cautious optimism on foundation models in medical imaging balancing privacy and innovation.NPJ digital medicine · 2026Article
- Understanding pre-training data effects in retinal foundation models using two large fundus cohorts.Nature communications · 2026Article
- A Generative Foundation Model for Scalable Cytology Image Synthesis in AI-Powered Diagnostics.Clinical cancer research : an official journal of the American Association for Cancer Research · 2026Article
- Classification of Inherited Retinal Diseases Using Artificial Intelligence Models for Fundus Autofluorescence and Ultrawide Retinal Images.Journal of ophthalmology · 2026Article
- 2025 in review.Nature biomedical engineering · 2025Article
- Synthetic data boosts medical foundation models.Nature biomedical engineering · 2025Article
- Generative Artificial Intelligence in Medical Imaging: Foundations, Progress, and Clinical Translation.Research (Washington, D.C.) · 2025Review
- A systematic review of vision and vision-language foundation models in ophthalmology.Advances in ophthalmology practice and researchReview
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
Foundation models are pretrained on massive datasets. However, collecting medical datasets is expensive and time-consuming, and raises privacy concerns. Here we show that synthetic data generated via conditioning with disease labels can be leveraged for building high-performing medical foundation models. We pretrained a retinal foundation model, first with approximately one million synthetic retinal images with physiological structures and feature distribution consistent with real counterparts, and then with only 16.7% of the 904,170 real-world colour fundus photography images required in a recently reported retinal foundation model (RETFound). The data-efficient model performed as well or better than RETFound across nine public datasets and four diagnostic tasks; and for diabetic-retinopathy grading, it used only 40% of the expert-annotated training data used by RETFound. We also support the generalizability of the data-efficient strategy by building a classifier for the detection of tuberculosis on chest X-ray images. The text-conditioned generation of synthetic data may enhance the performance and generalization of medical foundation models.
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Registered trials
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