Policy Implications of Artificial Intelligence and Machine Learning in Diabetes Management.

Journal: Current diabetes reports
Published Date:

Abstract

PURPOSE OF REVIEW: Machine learning (ML) is increasingly being studied for the screening, diagnosis, and management of diabetes and its complications. Although various models of ML have been developed, most have not led to practical solutions for real-world problems. There has been a disconnect between ML developers, regulatory bodies, health services researchers, clinicians, and patients in their efforts. Our aim is to review the current status of ML in various aspects of diabetes care and identify key challenges that must be overcome to leverage ML to its full potential.

Authors

  • David T Broome
    Department of Endocrinology, Diabetes & Metabolism, Cleveland Clinic Foundation, F-20 9500 Euclid Avenue, Cleveland, OH, 44195, USA.
  • C Beau Hilton
    Cleveland Clinic Lerner College of Medicine of Case Western Reserve University, 9500 Euclid Ave, Cleveland, OH, 44195, USA.
  • Neil Mehta
    Cleveland Clinic, USA.