AI can have a tremendous potential impact on healthcare by helping to improve diagnostic accuracy, broadening access to care, and easing administrative burden to enable care teams to focus on their patients. However, the field of healthcare is vast and there are more potential use-cases than developers can cover. In addition, AI development for health is particularly challenging because of the amount of data, expertise, and compute required to build models that reach the performance levels necessary for use in a clinical setting.
Without sufficiently diverse data — e.g., across patient populations, data acquisition devices, or protocols — models may not generalize well when deployed in environments that differ from the data on which they were trained. The resulting high barrier to entry prevents many would-be health AI developers from experimenting and makes it more difficult for them to take their ideas from concept to prototype, much less bench to bedside. For healthcare to continue to realize its potential, it needs innovation from a diverse set of contributors on a multitude of use-cases, interfaces and business models.
With this in mind, today we’re introducing Health AI Developer Foundations (HAI-DEF), a public resource to help developers build and implement AI models for healthcare more efficiently. Summarized in an accompanying technical report, HAI-DEF includes open-weight models, instructional Colab notebooks, and documentation to assist in every stage of development, from early research to commercial ventures.
HAI-DEF is part of our broader commitment to support healthcare AI development. It builds upon the Medical AI Research Foundations repository, released in 2023, which includes models for chest X-ray and pathology images. It also compliments initiatives like Open Health Stack, also launched in 2023, which provides developers with open-source building blocks for building effective health apps, and Population Dynamics Foundation Model, launched in 2024, which provides developers with geospatial embeddings to enable modeling of population-level changes including public health and beyond. By providing resources such as these, we aim to democratize AI development for healthcare, empowering developers to create innovative solutions that can improve patient care.