The U.S. government aims to turbocharge biomedical AI research.

What’s new: The National Institutes of Health, which invests $41.7 billion annually in medical research, announced a program called Bridge to Artificial Intelligence (Bridge2AI) to promote machine learning in human biology and medicine.

Take it to the bridge: The program’s primary goal is to develop new datasets. It also aims to standardize data from different sources and develop automated tools to help create datasets and ensure that they adhere to FAIR principles, which aim to enable machines to use data with little human intervention. Bridge2AI will fund research into two areas:

  • Creating datasets geared toward solving research “grand challenges” such as understanding how genes respond to disease, modeling physiological movement, and monitoring biological processes that lead from illness to health.
  • Establishing an administration center for Bridge2AI projects. The center will focus on developing best practices to meet grand-challenge goals in areas like teamwork, ethics, standards, tool optimization, and workforce development.
  • The NIH will begin accepting applications in June and will award funds the following spring.

Behind the news: U.S. government agencies bringing AI into mainstream healthcare.

  • A recent study estimated that U.S. regulators have approved 222 AI-powered medical devices.
  • The Food and Drug Administration released a plan to update its medical-device regulations for machine learning.
  • The Centers for Disease Control and Prevention uses machine learning to forecast annual flu outbreaks. Last year it deployed a chatbot to help screen people for Covid-19 infections.

Why it matters: Bigger, better datasets specially designed for machine learning could help illuminate human biological processes and the diseases that disrupt them.

We’re thinking: AI for medicine has tremendous potential. Datasets designed specifically to help us realize that potential may be just what the doctor ordered.

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