This paper is in the following e-collection/theme issue:

Digital Health Reviews (1458) Machine Learning (1662) Artificial Intelligence (1641) Wearable Devices and Sensors (968) mHealth for Wellness, Behavior Change and Prevention (3050) Web-based and Mobile Health Interventions (3550) Mobile Health (mhealth) (3110)

Published on in Vol 25 (2023)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/40259, first published .
The Effectiveness of Wearable Devices Using Artificial Intelligence for Blood Glucose Level Forecasting or Prediction: Systematic Review

The Effectiveness of Wearable Devices Using Artificial Intelligence for Blood Glucose Level Forecasting or Prediction: Systematic Review

The Effectiveness of Wearable Devices Using Artificial Intelligence for Blood Glucose Level Forecasting or Prediction: Systematic Review

Authors of this article:

Arfan Ahmed1 Author Orcid Image ;   Sarah Aziz1 Author Orcid Image ;   Alaa Abd-alrazaq1 Author Orcid Image ;   Faisal Farooq2 Author Orcid Image ;   Mowafa Househ3 Author Orcid Image ;   Javaid Sheikh1 Author Orcid Image
Article Authors Cited by (18) Tweetations (3) Metrics

Journals

  1. Ahmed A, Aziz S, Qidwai U, Abd-Alrazaq A, Sheikh J. Performance of artificial intelligence models in estimating blood glucose level among diabetic patients using non-invasive wearable device data. Computer Methods and Programs in Biomedicine Update 2023;3:100094 View
  2. Huang Y, Ni Z, Lu Z, He X, Hu J, Li B, Ya H, Shi Y. Heterogeneous temporal representation for diabetic blood glucose prediction. Frontiers in Physiology 2023;14 View
  3. Gonzalez-Rodriguez R, Hathaway E, Coffer J, del Castillo R, Lin Y, Cui J. Gold Nanoparticles in Porous Silicon Nanotubes for Glucose Detection. Chemosensors 2024;12(4):63 View
  4. Hou H, Zhang R, Li J. Artificial intelligence in the clinical laboratory. Clinica Chimica Acta 2024;559:119724 View
  5. Becker M, Matt C. How individuals perceive and process diagnostic device errors. Journal of Decision Systems 2024:1 View
  6. Kaladharan S, Manayath D, Gopalakrishnan R. Regulatory Challenges in AI/ML-Enabled Medical Devices: A Scoping Review and Conceptual Framework. Journal of Medical Devices 2024;18(4) View
  7. Yammouri G, Ait Lahcen A. AI-Reinforced Wearable Sensors and Intelligent Point-of-Care Tests. Journal of Personalized Medicine 2024;14(11):1088 View
  8. Morawski R. Teaching measurement science and technology in the times of pervasive AI. Measurement: Sensors 2024:101315 View
  9. Piao C, Zhu T, Baldeweg S, Taylor P, Georgiou P, Sun J, Wang J, Li K. GARNN: An interpretable graph attentive recurrent neural network for predicting blood glucose levels via multivariate time series. Neural Networks 2025;185:107229 View
  10. Du Z, Zhang F, Ge Y, Liu Y, Yu H, Wang Y, Dalan R, Shen X. Application of Wearable Devices in Diabetes Management. Health and Metabolism 2025:7 View
  11. Truong Duc P, Toan V. Wearable Fall Detection Device for Stroke Warning Based on IoT Technology and Convolutional Neural Network. Measurement: Interdisciplinary Research and Perspectives 2025:1 View
  12. El-Helaly M. Artificial Intelligence and Occupational Health and Safety, Benefits and Drawbacks. La Medicina del Lavoro 2024;115(2):e2024014 View
  13. Taherdoost H. Wearable Healthcare and Continuous Vital Sign Monitoring with IoT Integration. Computers, Materials & Continua 2024;81(1):79 View
  14. Martins O, Oosthuizen C, Desai D. Exploring the import of mechatronics engineering in medicine: a review. Beni-Suef University Journal of Basic and Applied Sciences 2025;14(1) View

Books/Policy Documents

  1. Longo I, D’Antoni F, Petrosino L, Piemonte V, Merone M, Pecchia L. 9th European Medical and Biological Engineering Conference. View
  2. Patel D, Dey A. Digital Health. View