AI-Assisted Cardiovascular Risk Assessment by General Practitioners in Resource-Constrained Indonesian Settings Using a Conceptual Prototype: Randomized Controlled Study
Improvements in risk assessment and statin prescription, coupled with reduced decision-making time, highlight the potential utility of AI in ASCVD risk assessment, particularly in resource-constrained settings where efficient use of health care resources and doctors' time is crucial. Further research is needed to ascertain whether improvements observed in this online study translate to real-world low-resource settings.
Authors
- Anwar Santoso (ORCID: https://orcid.org/0000-0001-8247-4151)
- Bambang Widyantoro (ORCID: https://orcid.org/0000-0001-5325-4125)
- Farah Magrabi (ORCID: https://orcid.org/0000-0002-8426-5588)
- Shlomo Berkovsky (ORCID: https://orcid.org/0000-0003-2638-4121)
- Dafsah Arifa Juzar (ORCID: https://orcid.org/0000-0001-8392-4759)
- David Lyell (ORCID: https://orcid.org/0000-0002-2695-0368)
- Anindya Pradipta Susanto (ORCID: https://orcid.org/0000-0001-5155-6904)
Institutions
- University of Indonesia (ID)
- Macquarie University (AU)
Publication Details
- Journal
- Journal of Medical Internet Research
- Published
- 2025-11-25
- DOI
- https://doi.org/10.2196/73131
- Primary Topic
- Artificial Intelligence in Healthcare and Education
- Type
- article
- Field-Weighted Citation Impact
- 0.00