Preliminary insights into artificial intelligence guided dosing in hypertension and diabetes: challenges and lessons learnt in a pilot feasibility study
This pilot demonstrates the feasibility of deploying CURATE.AI into outpatient care but underscores the importance of aligning data requirements with patient and clinical characteristics. Future studies should target newly diagnosed patient groups with greater dosing variability to optimise calibration and assess clinical utility.
Authors
- Satya Pavan Kumar Gollamudi
- Amartya Mukhopadhyay (ORCID: https://orcid.org/0000-0002-9747-5277)
- Lin Siew Chong
- Andre Teck Huat Tan
- Dylan Tan (ORCID: https://orcid.org/0000-0003-1480-1442)
- Jina Ali
- Mehul Motani (ORCID: https://orcid.org/0000-0003-3262-0207)
- S. H. Lee
- D. Heffernan Ho
- Gim Gee Teng
- Danliang Ho
- Santosh Kumar Seetharaman
- Lieng Hsi Ling
- Jennifer Sumner
Institutions
- Data Storage Institute (SG)
- National University of Singapore (SG)
- University Health System (US)
- Department of Health and Aged Care (AU)
- National University Heart Centre Singapore (SG)
- Alexandra Hospital (GR)
- Alexandra Hospital (GB)
- National University Hospital (SG)
- National University Health System (SG)
Publication Details
- Journal
- JAMIA Open
- Published
- 2026-01-06
- DOI
- https://doi.org/10.1093/jamiaopen/ooaf153
- Primary Topic
- Artificial Intelligence in Healthcare and Education
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- National University of Singapore