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Policy Implications of Artificial Intelligence and Machine Learning in Diabetes Management

  • Economics and Policy in Diabetes (AA Baig and N Laiteerapong, Section Editors)
  • Published:
Current Diabetes Reports Aims and scope Submit manuscript

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.

Recent Findings

ML has led to impressive progress in development of automated insulin delivery systems and diabetic retinopathy screening tools. Compared with these, use of ML in other aspects of diabetes is still at an early stage. The Food & Drug Administration (FDA) is adopting some innovative models to help bring technologies to the market in an expeditious and safe manner.

Summary

ML has great potential in managing diabetes and the future is in furthering the partnership of regulatory bodies with health service researchers, clinicians, developers, and patients to improve the outcomes of populations and individual patients with diabetes.

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References

Papers of particular interest, published recently, have been highlighted as: • Of importance •• Of major importance

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Authors and Affiliations

  1. Department of Endocrinology, Diabetes & Metabolism, Cleveland Clinic Foundation, F-20 9500 Euclid Avenue, Cleveland, OH, 44195, USA

    David T. Broome

  2. Cleveland Clinic Lerner College of Medicine of Case Western Reserve University, 9500 Euclid Ave, Cleveland, OH, 44195, USA

    C. Beau Hilton

  3. Cleveland Clinic Lerner College of Medicine of Case Western Reserve University, EC-40 9500 Euclid Ave, Cleveland, OH, 44195, USA

    Neil Mehta

Authors
  1. David T. Broome
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  2. C. Beau Hilton
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  3. Neil Mehta
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Correspondence to Neil Mehta.

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This article is part of the Topical Collection on Economics and Policy in Diabetes

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Broome, D.T., Hilton, C.B. & Mehta, N. Policy Implications of Artificial Intelligence and Machine Learning in Diabetes Management. Curr Diab Rep 20, 5 (2020). https://doi.org/10.1007/s11892-020-1287-2

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  • DOI: https://doi.org/10.1007/s11892-020-1287-2

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