First global medical AI foundation model drives 100-country research collaboration

A global research consortium of over 100 study groups in more than 65 countries has launched the Global RETFound initiative, a collaborative effort to develop the first globally representative Artificial Intelligence (AI) foundation model in medicine, using 100 million eye images.

As described in Nature Medicine, the initiative is one of the largest medical AI collaborations ever undertaken, producing one of the most geographically and ethnically diverse medical datasets assembled for AI training purposes. The data will span Africa, the Middle East, South America, Southeast Asia, the Western Pacific, and the Caucasus region.

Led by researchers from the National University of Singapore Yong Loo Lin School of Medicine (NUS Medicine), Moorfields NHS Foundation Trust, University College London (UCL), and the Chinese University of Hong Kong (CUHK), the consortium will develop its model using an unprecedented dataset of over 100 million color fundus photographs (photos of the back of the eye), sourced from more than 65 countries.

The global initiative builds on the success of RETFound, the first foundation model for retinal and systemic disease detection. Published in Nature in 2023, RETFound was originally developed by researchers at Moorfields Eye Hospital and UCL Institute of Ophthalmology in London. The proof-of-concept study involved a smaller scale of 1.6 million fundus photographs curated by the INSIGHT Health Data Research Hub at Moorfields.

While RETFound demonstrated potential for medical AI applications, the next global model will expand the training data to encompass every continent except Antarctica.

“Current foundational models are trained on data that is geographically and demographically ‘narrow’, which limits their effectiveness and can perpetuate existing health inequalities,” explained Dr. Yih Chung Tham, Assistant Professor at NUS Medicine, and a NUS Presidential Young Professor, one of the key project leads. “The Global RETFound Consortium addresses this challenge through innovative approaches that enable broad international participation at unprecedented scale, while maintaining data privacy protections.”

A key innovation of the project is its flexible, two-pronged data sharing framework, designed to accommodate varying technical capacities and regulatory requirements across participating institutions. The first approach involves local fine-tuning of generative AI models at individual institutions, with only model weights shared centrally, ensuring no patient data leaves the originating site. The second pathway enables direct sharing of de-identified data through secure infrastructure for institutions that do not have local GPU resources or technical expertise.

“This dual approach allows participation from research groups regardless of their resource levels,” noted Pearse Keane, Professor of Artificial Medical Intelligence at UCL. “By combining real and synthetic data generation techniques, we can build a diverse, globally representative dataset without compromising security.”

Prof. Carol Cheung from The Chinese University of Hong Kong emphasized the broader implications: “This initiative has the potential to establish new international benchmarks for generalizability and fairness in medical AI. By providing researchers worldwide with access to a ‘globally-trained’ foundation model, we can accelerate development of AI tools tailored to local clinical needs with substantially reduced data and computational requirements.”

Dr. Tham added, “The Global RETFound model will undergo comprehensive evaluation across multiple ophthalmic and systemic diseases, including diabetic retinopathy, glaucoma, age-related macular degeneration and cardiovascular diseases. The model will be released under a Creative Commons license, making it freely and publicly available for non-commercial research use worldwide.”

While ophthalmology serves as the initial blueprint for such a collaborative framework, the researchers aim to share their methodologies widely, laying the groundwork for similar global initiatives across other medical specialties.

The project addresses growing concerns about AI bias in health care while demonstrating how international collaboration can advance medical AI development in an equitable way. The consortium welcomes additional researchers and institutions to join their collaborative effort towards more inclusive medical AI development.