Federated AI, Current State, and Future Potential.

Journal: Asia-Pacific journal of ophthalmology (Philadelphia, Pa.)
Published Date: May 31, 2023

Abstract

Artificial intelligence and machine learning applications are becoming increasingly popular in health care and medical devices. The development of accurate machine learning algorithms requires large quantities of good and diverse data. This poses a challenge in health care because of the sensitive nature of sharing patient data. Decentralized algorithms through federated learning avoid data aggregation. In this paper we give an overview of federated learning, current examples in healthcare and ophthalmology, challenges, and next steps.

Authors

  • Phoebe Clark
    Department of Population Health, NYU Langone Health, New York City, NY.
  • Eric K Oermann
    Icahn School of Medicine at Mount Sinai, New York, NY, USA.
  • Dinah Chen
    NYU Langone Health, Department of Ophthalmology, New York University School of Medicine, New York, New York.
  • Lama A Al-Aswad
    Columbia University Medical Center, Harkness Eye Institute, New York, New York, USA. Electronic address: laa2003@cumc.columbia.edu.

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