Robust medical Machine Learning (ML) models have the potential to revolutionize healthcare by accelerating clinical research, improving workflows and outcomes, and producing novel insights or capabilities. Developing such ML models from scratch is cost prohibitive and requires substantial compute, data, and time (e.g., expert labeling). To address these challenges, we introduce Health AI Developer Foundations (HAI-DEF), a suite of pre-trained, domain-specific foundation models, tools, and recipes to accelerate building ML for health applications. The models cover various modalities and domains, including radiology (X-rays and computed tomography), histopathology, dermatological imaging, and audio. These models provide domain specific embeddings that facilitate AI development with less labeled data, shorter training times, and reduced computational costs compared to traditional approaches. In addition, we utilize a common interface and style across these models, and prioritize usability to enable developers to integrate HAI-DEF efficiently. We present model evaluations across various tasks and conclude with a discussion of their application and evaluation, covering the importance of ensuring efficacy, fairness, and equity. Finally, while HAI-DEF and specifically the foundation models lower the barrier to entry for ML in healthcare, we emphasize the importance of validation with problem- and population-specific data for each desired usage setting. This technical report will be updated over time as more modalities and features are added.

Driving is a sensorimotor task that is done often, and requires a degree of competency that has to be taught. While daily driving is complex and safety critical, performance driving requires a higher degree of competency in handling the vehicle at high speeds and limits of stability and requires years of one-on-one instruction and practice to master. Although driving instructors can help drivers perform better and safer [1], their availability is limited and costly. Hence, there is a clear need for automated teaching which can help drivers improve at the population scale. Driving instructors, e.g. in performance track driving [2], rely on their expertise in the driving task and their inference of student's skill levels to effectively teach students of various skill levels and learning styles. Instructors can gauge their students' skill levels and estimate what a student might do in a given scenario to provide contextually-relevant verbal instructions to the student. For example, consider how an instructor in the passenger seat might instruct a student driver on the appropriate timing for braking or the lateral positioning of the car with respect to the racing line (the optimal minimum time path around a race course). The teacher's ability to judge whether the student can maintain the racing line or oversteer in a turn influences what instructions are provided. An automated teaching system for driving should be able to take in relevant vehicle context (pose and dynamics, map information, etc.) and other factors (eg., driver monitoring) as inputs and output appropriate teaching actions for the