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npj Digital Medicine· 2025Q1· Review

Integration of artificial intelligence and wearable technology in the management of diabetes and prediabetes

Raphael André Fraser, Rebekah J. Walker, Jennifer Annette Campbell, Obinna Ikechukwu Ekwunife et al.

Short summary

A systematic review of 60 studies (from >5000 records) found that integrating AI with wearable technology significantly improves diabetes management, showing promise in glycemic monitoring, adaptive insulin delivery, and event prediction.

AI-generated from the title and abstract; the full text is not read.

Key points

  • AI and wearables integration shows promise in glycemic monitoring, adaptive insulin management, and predicting diabetes-related events.
  • Wearable devices enhance self-management and inform clinical decision-making in diabetes care.
  • Sixty studies were included in the review, analyzing over 5000 records.
  • Key challenges include limited demographic diversity, variable data quality, and lack of standardized AI performance benchmarks.

AI-generated from the title and abstract; the full text is not read.

Abstract

Artificial intelligence and wearable technology are increasingly used in healthcare and hold significant potential for improving the management of diabetes. Wearable devices enable continuous monitoring and real-time data collection, supporting AI-driven personalized interventions. This systematic review evaluated peer-reviewed studies that examined the integration of AI and wearable technology in diabetes management, with a focus on clinical and self-management outcomes. Sixty studies were included following a review of over 5000 records. AI models paired with wearable devices showed promise in glycemic monitoring, adaptive insulin management, and predicting diabetes-related events. Continuous glucose monitors and other wearables also enhanced self-management and informed clinical decision-making. However, key challenges persist, including limited demographic diversity, variable data quality, a lack of standardized benchmarks for evaluating AI performance, and limited interpretability of complex models. Future research should prioritize improving model transparency, addressing demographic disparities, and establishing clear benchmarks to support equitable and effective implementation in diabetes care.

The authors' abstract, as published at the source. npj Digital Medicine, 2025 · DOI ↗

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Field: General Health Professions

General Health ProfessionsHealth Professions