AI has the potential to have a huge potential impact on healthcare by improving diagnostic accuracy, expanding access to treatment, and reducing administrative burden so healthcare teams can focus on patients. There is a gender. However, the healthcare field is vast and there are more potential use cases than developers can cover. Additionally, AI development for healthcare is particularly challenging because building models that reach the performance levels required for use in clinical settings requires large amounts of data, expertise, and computing.
Without sufficiently diverse data, such as patient populations, data collection devices, and protocols, models may not generalize well if they are deployed in environments different from the data on which they were trained. The resulting high barriers to entry prevent many would-be health AI developers from experimenting, making it even more difficult to move ideas from concept to prototype, much less from bench to bedside. For healthcare to continue to realize its potential, it will require innovation from diverse contributors across numerous use cases, interfaces, and business models.
With this in mind, today we are introducing Health AI Developer Foundations (HAI-DEF), a public resource that helps developers build and implement medical AI models more efficiently. Summarized in an accompanying technical report, HAI-DEF includes open weight models, explanatory Colab notebooks, and documentation to support every stage of development, from early research to commercial ventures.
HAI-DEF is part of our broader commitment to supporting healthcare AI development. It builds on the Medical AI Research Foundations repository released in 2023 and includes models for chest X-ray and pathology images. Also launching in 2023 is Open Health Stack, which will provide developers with open source building blocks for building effective health apps, and Geography, which will launch in 2024 and will enable population modeling. It also complements efforts such as the Population Dynamics Foundation Model, which provides spatial embedding to developers. -Changes in levels, including public health, etc. By providing resources like this, we aim to democratize healthcare AI development and empower developers to create innovative solutions that can improve patient care.
