From PhysioNet to foundation models—a history and potential futures

Abstract Over the last 35 years, the sharing of medical data and models for research has evolved from sneakernet to the internet—from mailing magnetic tapes and compact discs of a handful of well-curated recordings, to the high-speed download of relatively comprehensive hospital databases. More recently, the fervor around the potential for modern machine learning and ‘AI’ to catapult us into the next industrial revolution has led to a seemingly insatiable desire to pump almost any source of data into large models. Although this has great potential, it also presents a whole set of new challenges. In this article I examine these trends over the last 30 years, drawing on examples from the world of physiology, and in particular, cardiology, since it is one of the oldest data-intensive fields, and is undergoing a renaissance in the context of machine learning. From the early days of computerized cardiology, the Research Resource for Complex Physiologic Signals ( PhysioNet ) has been at the cutting edge of this field. This article, therefore, includes much of the Resource’s history and the contributions of many key people involved, drawn from almost three decades of firsthand experience of co-developing elements of the Resource with its founders. I address what I perceive to be the most promising future directions for the PhysioNet Resource, and more generally, the growing issues and opportunities around dissemination and use of massive physiological databases, associated open access code, and public competitions, together with potential solutions to the key issues that face our field. Topics range from how we should approach foundation models in the context of the rapidly growing AI carbon footprint, to the potential of Tiny-ML and edge computing. I also cover issues around prizes and incentives, funding models, and scientific repeatability, as well as how we might address these issues by leveraging the PhysioNet Challenges, consistent with the philosophy of open-access from the early days of the PhysioNet Resource. Since this article is almost 38 pages long, I have added a single-page ten-point summary at the end of the article.