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

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

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