Artificial Intelligence for Diabetes Management and Decision Support: Literature Review

Abstract

Journal of Medical Internet Research - International Scientific Journal for Medical Research, Information and Communication on the Internet #Preprint #PeerReviewMe: Warning: This is a unreviewed preprint. Readers are warned that the document has not been peer-reviewed by expert/patient reviewers or an academic editor, may contain misleading claims, and is likely to undergo changes before final publication, if accepted, or may have been rejected/withdrawn. Readers with interest and expertise are encouraged to sign up as peer-reviewer, if the paper is within an open peer-review period. Please cite this preprint only for review purposes or for grant applications and CVs (if you are the author). Background: Artificial intelligence (AI) methods in combination with the latest technologies, including medical devices, mobile computing, and sensor technologies, have the potential to enable the creation and delivery of better management services to deal with chronic diseases. One of the most lethal and prevalent chronic diseases is diabetes mellitus, which is characterized by dysfunction of glucose homeostasis. Objective: To review recent efforts to use AI techniques to assist in the management of diabetes, along with the associated challenges. Methods: We conducted a review of the literature using PubMed and related bibliographic resources. Analyses of the literature from 2010 to 2018 yielded 2641 pertinent articles, of which we selected 113 for detailed review. Results: We propose a functional taxonomy for diabetes management and AI. We also performed a detailed analysis of each subject category using related key outcomes. This approach revealed that the experiments and studies reviewed yielded encouraging results. Conclusions: We obtained evidence of an acceleration of research activity aimed at developing AI-powered tools for prediction and prevention of complications associated with diabetes. Our results indicate that AI methods are being progressively established as suitable for use in clinical daily practice, as well as for the self-management of diabetes. Consequently, these methods provide powerful tools for improving patients’ quality of life.