ACD Working Group on Artificial Intelligence


Artificial Intelligence (AI), machine learning (ML), and deep learning (DL) are being integrated into many areas of biomedical and clinical research. Most NIH institutes and centers have some investments in AI, ML or DL; for example, to develop these technologies, use them to assist in research processes, or apply them to assist with clinical diagnostics and decisions, among other applications. Overall at NIH, these technologies are cross-cutting, with individual investments and specific applications to diseases or disciplines varying from institute to institute. But there is no overarching strategic plan, and there is concern that NIH is underinvesting in an area that has enormous promise for biomedical research. As AI/ML/DL are more heavily integrated into biomedical and clinical research and medicine, NIH is committed to generating cross-agency efforts in these areas.

As an initial step in exploring the latest developments in AI related to biomedicine, NIH supported a workshop in July 2018, Harnessing Artificial Intelligence and Machine Learning to Advance Biomedical Research, to engage experts and gain insight on the biggest challenges and opportunities. The discussions were vibrant, but further added to the conviction that NIH needs to invest wisely and broadly in this area. To continue interactions with the AI community and encourage deeper interactions between experts from biomedicine and data science-related fields, the NIH Director has formed the Advisory Committee to the Director working group on Artificial Intelligence.


  • Determine opportunities for innovative cross-NIH efforts in artificial intelligence, and identify ways for these efforts to reach broadly across biomedical topics and have positive effects across many diverse fields, diseases, and research communities.
  • Identify mechanisms for NIH to build lasting connections and collaborations between the computer and data science communities and the biomedical research community, to establish ongoing interactions that push both fields forward.
  • Define approaches, including training mechanisms and creative career pipelines, that NIH can take to encourage computer scientists to work in biomedical research environments, and to better train biomedical researchers in computer science-related disciplines.
  • Identify the major ethical considerations as they relate to biomedical research and using AI and related technologies for health-related research and care, and suggest ways that NIH can build these considerations into its AI-related programs and activities.

Related Resources


  • Rediet Abebe
    Cornell University
  • Greg Corrado, Ph.D.
  • Kate Crawford, Ph.D.
    AI Now Institute
    New York University
  • Barbara Engelhardt, Ph.D.
    Princeton University
  • David Haussler, Ph.D.
    University of California, Santa Cruz
  • Dina Katabi, Ph.D.
    Massachusetts Institute of Technology
  • Daphne Koller, Ph.D.
  • Anshul Kundaje, Ph.D.
    Stanford University
  • Eric Lander, Ph.D.
    Broad Institute
  • Jennifer Listgarten, Ph.D.
    University of California, Berkeley
  • Michael McManus, Ph.D.
  • Serena Yeung, Ph.D.
    Stanford University


  • David Glazer
  • Lawrence Tabak, DDS, Ph.D.
    National Institutes of Health


  • Jessica Mazerik, Ph.D.
    National Institutes of Health

This page last reviewed on January 29, 2019