AI has the potential to have a huge impact on healthcare by helping to improve diagnostic accuracy, expand access to care, and reduce administrative burden so healthcare teams can focus on their patients. However, the healthcare field is vast and there are more potential use cases than developers can cover. Additionally, developing AI for healthcare is particularly challenging due to the amount of data, expertise, and computation required to create models that achieve the performance levels necessary for use in clinical settings.
Without sufficiently diverse data (e.g., on patient populations, data acquisition devices, or protocols), models may not generalize well when deployed in environments different from the data on which they are based. been trained. The resulting high barrier to entry prevents many potential health AI developers from experimenting and makes it more difficult for them to take their ideas from concept to prototype, let alone from bench to bedside. For healthcare to continue to realize its potential, it needs innovation from a diverse set of contributors across a multitude of use cases, interfaces and business models.
With this in mind, today we present Healthcare AI Developer Foundations (HAI-DEF), a public resource to help developers more effectively create and implement AI models for healthcare. Summary in an accompaniment technical reportHAI-DEF includes open models, Colab teaching workbooks, and documentation to help you through every stage of development, from early research to commercial projects.
HAI-DEF is part of our broader commitment to supporting the development of AI in healthcare. It is based on the Medical AI Research Foundations repository, published in 2023, which includes chest x-ray and pathology image templates. It also complements initiatives such as Open Health Stackalso launched in 2023, which provides developers with open source building blocks to create effective health applications, and Basic model of population dynamicslaunching in 2024, which provides developers with geospatial integrations to enable modeling of population-level changes, including public health and beyond. By providing resources like these, we aim to democratize the development of AI for healthcare, enabling developers to create innovative solutions that can improve patient care.