Multimodal biomedical AI.

Journal: Nature medicine
Published Date: Sep 15, 2022

Abstract

The increasing availability of biomedical data from large biobanks, electronic health records, medical imaging, wearable and ambient biosensors, and the lower cost of genome and microbiome sequencing have set the stage for the development of multimodal artificial intelligence solutions that capture the complexity of human health and disease. In this Review, we outline the key applications enabled, along with the technical and analytical challenges. We explore opportunities in personalized medicine, digital clinical trials, remote monitoring and care, pandemic surveillance, digital twin technology and virtual health assistants. Further, we survey the data, modeling and privacy challenges that must be overcome to realize the full potential of multimodal artificial intelligence in health.

Authors

  • Julián N Acosta
    From the Department of Neurology, Yale School of Medicine, New Haven, Conn (J.N.A., G.J.F.); and Department of Biomedical Informatics, Harvard Medical School, 10 Shattuck St, Boston, MA 02115 (P.R.).
  • Guido J Falcone
    Department of Neurology (G.J.F., E.P.K., R.B.N., K.R., J.A., K.N.S.), Yale School of Medicine, New Haven, CT.
  • Pranav Rajpurkar
    Harvard Medical School, Department of Biomedical Informatics, Cambridge, MA, 02115, US.
  • Eric J Topol
    Scripps Research Translational Institute, La Jolla, CA 92037, USA; Scripps Clinic Division of Cardiovascular Diseases, La Jolla, CA 92037, USA. Electronic address: etopol@scripps.edu.

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