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Companies, hospitals and funders would form a consortium, like the Structural Genomics Consortium or IMI, to train one multimodal AI on scans, slides, genomes and outcomes from millions of patients by federated training across dozens of health systems, with the data never leaving the hospitals. Members would share the base model and compete on applications built on it.
The existing idea of patient-level multimodal foundation models for treatment selection depends on data no single organisation holds. The proposal is the governance and infrastructure to build one as shared infrastructure: a consortium (like the Structural Genomics Consortium or IMI) with federated training across dozens of health systems, pre-agreed data-use terms, open or consortium-licensed weights, a neutral host, and evaluation on sequestered prospective data. Members compete on applications built on top, not on the base model.
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Shares PathAI, Paige AI, How medical AI devices are evaluated: limitations and recommendations from an analysis of FDA approvals, AI that is built but not validated or deployed.
Shares PathAI, Paige AI, Pathology & radiology foundation models, AI that is built but not validated or deployed.
Shares Patient-level multimodal foundation models for treatment selection, How medical AI devices are evaluated: limitations and recommendations from an analysis of FDA approvals, Pathology & radiology foundation models, AI that is built but not validated or deployed.
Shares PathAI, Owkin, Paige AI, Pathology & radiology foundation models.
Shares PathAI, Paige AI, Pathology & radiology foundation models, AI that is built but not validated or deployed.
Shares How medical AI devices are evaluated: limitations and recommendations from an analysis of FDA approvals, Pathology & radiology foundation models, AI that is built but not validated or deployed, Digital pathology & AI.
Shares Pathology & radiology foundation models, AI that is built but not validated or deployed, Digital pathology & AI, Data silos.