{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,12]],"date-time":"2026-01-12T14:39:22Z","timestamp":1768228762397,"version":"3.49.0"},"reference-count":38,"publisher":"Oxford University Press (OUP)","issue":"1","license":[{"start":{"date-parts":[[2026,1,12]],"date-time":"2026-01-12T00:00:00Z","timestamp":1768176000000},"content-version":"vor","delay-in-days":6,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Health Innovation Programme, National University of Singapore and National University of Singapore Health System","award":["CF\/THK\/19\/13"],"award-info":[{"award-number":["CF\/THK\/19\/13"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026,1,6]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>CURATE.AI is an artificial intelligence platform enabling personalised drug dosing. Aims:<\/jats:p>\n                    <jats:p>1) Determine the feasibility of using CURATE.AI in the outpatient setting.<\/jats:p>\n                    <jats:p>2) Compare the consistency of CURATE.AI recommendations derived from different data sources.<\/jats:p>\n                    <jats:p>3) Assess the alignment of physician and CURATE.AI dosing recommendations.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Materials and Methods<\/jats:title>\n                    <jats:p>We conducted a single-arm feasibility study involving type II diabetics and hypertensives recruited from a hospital\u2019s outpatient clinics. Outcomes included recruitment and study completion rates, adherence to study protocols, patient satisfaction, consistency of CURATE.AI recommendations across data sources, and alignment with physicians\u2019 dosing decisions. We calibrated CURATE.AI for each individual using three distinct datapoints that linked drug dose to clinical response. After calibration, participants entered a four-month active phase, receiving monthly CURATE.AI dosing recommendations.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Eighteen participants were recruited, and thirteen completed the study. Only three progressed to the active study phase, primarily due to insufficient dose adjustments required during the calibration phase. Adherence to scheduled visits was 76% and adherence to home monitoring averaged 81%. Barriers to adherence included technical issues and work-related conflicts. Participants expressed high satisfaction with monitoring and care \u226588%. Actionable dosing recommendations were generated for two of the three participants, with varying alignment to physician decisions depending on the data source used.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Discussion<\/jats:title>\n                    <jats:p>Calibration challenges emerged when applying AI-guided dosing in a chronic disease population. Limited dose titration opportunities and cautious clinical practice restricted the data generation needed for effective model calibration.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>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.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1093\/jamiaopen\/ooaf153","type":"journal-article","created":{"date-parts":[[2026,1,12]],"date-time":"2026-01-12T11:30:20Z","timestamp":1768217420000},"source":"Crossref","is-referenced-by-count":0,"title":["Preliminary insights into artificial intelligence guided dosing in hypertension and diabetes: challenges and lessons learnt in a pilot feasibility study"],"prefix":"10.1093","volume":"9","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2200-3275","authenticated-orcid":false,"given":"Jennifer","family":"Sumner","sequence":"first","affiliation":[{"name":"Medical Affairs \u2013 Research, Innovation & Enterprise, Alexandra Hospital, National University Health System 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,","place":["Singapore"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-7233-7140","authenticated-orcid":false,"given":"Danliang","family":"Ho","sequence":"additional","affiliation":[{"name":"Department of Electrical & Computer Engineering, National University of Singapore ,","place":["Singapore"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dylan Hong Tai","family":"Tan","sequence":"additional","affiliation":[{"name":"Department of Electrical & Computer Engineering, National University of Singapore ,","place":["Singapore"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6063-8530","authenticated-orcid":false,"given":"Lieng Hsi","family":"Ling","sequence":"additional","affiliation":[{"name":"Yong Loo Lin School of Medicine, National University of Singapore ,","place":["Singapore"]},{"name":"Department of Cardiology, National University Heart Centre 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