Cross-sectional design and protocol for Artificial Intelligence Ready and Equitable Atlas for Diabetes Insights (AI-READI).

Journal: BMJ open
PMID: 39915016

Abstract

INTRODUCTION: Artificial Intelligence Ready and Equitable for Diabetes Insights (AI-READI) is a data collection project on type 2 diabetes mellitus (T2DM) to facilitate the widespread use of artificial intelligence and machine learning (AI/ML) approaches to study salutogenesis (transitioning from T2DM to health resilience). The fundamental rationale for promoting health resilience in T2DM stems from its high prevalence of 10.5% of the world's adult population and its contribution to many adverse health events.

Authors

  • Cynthia Owsley
    Department of Ophthalmology and Visual Sciences, School of Medicine, University of Alabama at Birmingham, Birmingham, AL, USA.
  • Dawn S Matthies
    Ophthalmology and Visual Sciences, The University of Alabama at Birmingham, Birmingham, Alabama, USA.
  • Gerald McGwin
    Ophthalmology and Visual Sciences, The University of Alabama at Birmingham, Birmingham, Alabama, USA.
  • Jeffrey C Edberg
    Medicine, University of Alabama at Birmingham, Birmingham, Alabama, USA.
  • Sally L Baxter
    Viterbi Family Department of Ophthalmology, University of California, San Diego, La Jolla.
  • Linda M Zangwill
    Hamilton Glaucoma Center, Viterbi Family Department of Ophthalmology and Shiley Eye Institute, University of California, San Diego, La Jolla, California.
  • Julia P Owen
    Department of Ophthalmology, University of Washington, Seattle, Washington.
  • Cecilia S Lee
    Department of Ophthalmology, University of Washington, Seattle, Washington, USA.

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