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Med."],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Federated medical AI revolutionizes multi-center collaboration, while communication cost, data scarcity, and heterogeneity still limit its practical deployment. Foundation models (FMs) offer a promising avenue for addressing these challenges, owing to their generalization capabilities and efficient adaptability to medical tasks. Here, we present Federated Generative Prompt Learning (Fed-GPL), a universal and efficient framework for multi-center medical image analysis. It collaboratively trains a prompt generator that produces customized prompts for each patient, capturing patient-specific variations and enabling precise medical diagnosis. Fed-GPL is compatible with various vision FMs and medical tasks, such as Vision Transformer (ViT) for diabetic retinopathy and melanoma classification, and Segment Anything (SAM) for polyp and prostate segmentation. Fed-GPL outperforms traditional models and full fine-tuning methods, with only 8.26% and 6.55% of the total FM parameters being trained across classification and segmentation tasks, while converging within just 15 communication rounds. For low-resource settings, Fed-GPL maintains its performance with 5% of the original training data.<\/jats:p>","DOI":"10.1038\/s41746-026-02866-1","type":"journal-article","created":{"date-parts":[[2026,6,10]],"date-time":"2026-06-10T00:44:39Z","timestamp":1781052279000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Federated generative prompt learning with vision foundation models: universal efficient multi-center medical image analysis"],"prefix":"10.1038","volume":"9","author":[{"given":"Xi","family":"Lin","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuliang","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jun","family":"Wu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chang","family":"Xu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jianhua","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiu","family":"Su","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,6,10]]},"reference":[{"key":"2866_CR1","doi-asserted-by":"publisher","first-page":"600","DOI":"10.55730\/1300-0152.2766","volume":"49","author":"T Banerjee","year":"2025","unstructured":"Banerjee, T. & Pa\u00e7al, \u0130 A systematic review of machine learning in heart disease prediction. 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