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

Formative Evaluation of Digital Health Interventions (2823) Artificial Intelligence (1740) Machine Learning (1738) Mobile Health (mhealth) (3158) Artificial Intelligence in Diabetes Care and Prevention (38) Apps, Mobile, Wearables for Diabetes (333) Diabetes Prevention (99)

Published on in Vol 7 (2023)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/50328, first published .
A Mobile App That Addresses Interpretability Challenges in Machine Learning–Based Diabetes Predictions: Survey-Based User Study

A Mobile App That Addresses Interpretability Challenges in Machine Learning–Based Diabetes Predictions: Survey-Based User Study

A Mobile App That Addresses Interpretability Challenges in Machine Learning–Based Diabetes Predictions: Survey-Based User Study

Authors of this article:

Rasha Hendawi1 Author Orcid Image ;   Juan Li1 Author Orcid Image ;   Souradip Roy1 Author Orcid Image
Article Authors Cited by (5) Tweetations (2) Metrics

Journals

  1. Yousef H, Feng S, Jelinek H. Exploratory risk prediction of type II diabetes with isolation forests and novel biomarkers. Scientific Reports 2024;14(1) View
  2. Saarela M, Podgorelec V. Recent Applications of Explainable AI (XAI): A Systematic Literature Review. Applied Sciences 2024;14(19):8884 View
  3. Hasan R, Dattana V, Mahmood S, Hussain S. Towards Transparent Diabetes Prediction: Combining AutoML and Explainable AI for Improved Clinical Insights. Information 2024;16(1):7 View
  4. Tao Y, Hou J, Zhou G, Zhang D. Artificial intelligence applied to diabetes complications: a bibliometric analysis. Frontiers in Artificial Intelligence 2025;8 View
  5. Queipo-de-Llano E, Ciurcau M, Paz-Olalla A, Díaz-Agudo B, Recio-García J. eXplainable Artificial Intelligence for Hip Fracture Recognition. Applied Artificial Intelligence 2025;39(1) View