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

User-Centered Design Case Studies (852) Decision Support for Health Professionals (2268) Usability Evaluation Case Studies (361) Design and Formative Evaluation of Mobile Apps (1014) Diabetes (343) Design and Usability of Clinical Software and EHRs (469) Diabetes Self-Management (731) Diabetes-specific EHR Improvements (41) Artificial Intelligence (4852)

Published on in Vol 11 (2024)

This is a member publication of National University of Singapore

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/50939, first published .
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Assessing the Utility, Impact, and Adoption Challenges of an Artificial Intelligence–Enabled Prescription Advisory Tool for Type 2 Diabetes Management: Qualitative Study

Assessing the Utility, Impact, and Adoption Challenges of an Artificial Intelligence–Enabled Prescription Advisory Tool for Type 2 Diabetes Management: Qualitative Study

Authors of this article:

Sungwon Yoon1, 2 Author Orcid Image ;   Hendra Goh1 Author Orcid Image ;   Phong Ching Lee3 Author Orcid Image ;   Hong Chang Tan3 Author Orcid Image ;   Ming Ming Teh3 Author Orcid Image ;   Dawn Shao Ting Lim3 Author Orcid Image ;   Ann Kwee3 Author Orcid Image ;   Chandran Suresh3 Author Orcid Image ;   David Carmody3 Author Orcid Image ;   Du Soon Swee3 Author Orcid Image ;   Sarah Ying Tse Tan3 Author Orcid Image ;   Andy Jun-Wei Wong3 Author Orcid Image ;   Charlotte Hui-Min Choo3 Author Orcid Image ;   Zongwen Wee3 Author Orcid Image ;   Yong Mong Bee3 Author Orcid Image
Article Authors Cited by (10) Tweetations (2) Metrics

Journals

  1. He Z, Li W. AI-Driven Management of Type 2 Diabetes in China: Opportunities and Challenges. Diabetes, Metabolic Syndrome and Obesity 2025;Volume 18:85 View
  2. Tun H, Rahman H, Naing L, Malik O. Trust in Artificial Intelligence–Based Clinical Decision Support Systems Among Health Care Workers: Systematic Review. Journal of Medical Internet Research 2025;27:e69678 View
  3. González-Rivas J, Seyedi S, Mechanick J. Artificial Intelligence Enabled Lifestyle Medicine in Diabetes Care: A Narrative Review. American Journal of Lifestyle Medicine 2025 View
  4. Parsons C, Zuiderwijk A, Orchard N, Oosterhoff J, de Reuver M. Task-Technology Fit of Artificial Intelligence-based clinical decision support systems: a review of qualitative studies. BMC Medical Informatics and Decision Making 2025;25(1) View
  5. Li W, Li L. Artificial intelligence in mobile health applications: A comprehensive review of its role in diabetes care. World Journal of Methodology 2026;16(1) View
  6. Kon M, Abisheganaden J, Ang G. How clinicians should prioritise the use of artificial intelligence in Singapore’s healthcare system: Correspondence. Annals of the Academy of Medicine Singapore 2026;55(5):289 View
  7. Goh H, Khuon D, Ung M, Oy S, Dary C, Khim C, Chhay S, Lee Y, Kim R, Yi S, Saphonn V. i-MoMCARE: AI-enabled mobile app for maternal and child health care in Cambodia – a pilot implementation and evaluation study. BMJ Health & Care Informatics 2026;33(1):e101691 View
  8. Hu H, LI R, Yin Y, Liu Y, Wang X. Human factors engineering in intelligent medical devices: paradigm evolution, core issues and frontier prospects. Frontiers in Industrial Engineering 2026;4 View

Books/Policy Documents

  1. Karim A, Ghaffar F, Xu W, Furtado N, Burns C. HCI International 2026 Posters. View
  2. Asrifan A, Patinrosi N, Sahabuddin S. Cybersecurity, AI Collaboration, and Software Practice for Healthcare. View