Key result
AI-assisted antihypertensive titration proves feasible and safe but shows no significant SBP reduction over standard care.
Why the study?
Fewer than 1 in 4 patients with hypertension achieve BP targets, partly due to non-personalised dosing strategies. The safety and clinical feasibility of integrating CURATE.AI, an individualised AI-assisted clinical decision support system, for antihypertensive dose titration in routine outpatient care required evaluation.
Does integrating CURATE.AI, an individualised AI-assisted clinical decision support system, with home blood pressure monitoring provide safe and clinically feasible antihypertensive dose titration compared to standard care or home blood pressure monitoring alone in patients with primary hypertension?
RCT (n=45)
Open-label
1:1:1
Does integrating CURATE.AI, an individualised AI-assisted clinical decision support system, with home blood pressure monitoring provide safe and clinically feasible antihypertensive dose titration compared to standard care or home blood pressure monitoring alone in patients with primary hypertension?
This proof-of-concept pilot trial demonstrates that AI-assisted personalised titration of antihypertensive medication using CURATE.AI is safe and clinically feasible, supporting the conduct of a larger trial powered for efficacy.
Captured external expert commentary on this paper, strongest first. Original sources are linked where available.
“In the long run, if we can use this system, it would mean that my patients can achieve their blood pressure targets faster even though they don't see me in person as often.”
“Clinicians will need to be trained to use and interpret the recommendations from these tools.”
High adherence supports home monitoring feasibility for AI titration; extends individualized dosing research but leaves efficacy open.
Background Hypertension is the leading modifiable cardiovascular risk factor for global mortality and disability1. Despite effective pharmacological therapies, fewer than 1 in 4 patients achieve blood pressure (BP) targets2. This discrepancy may be partly attributed to the limitations of standardised but non-personalised dosing strategies3. Digital health and artificial intelligence (AI) platforms such as CURATE.AI offer an opportunity to move beyond limitations of population-based, "one-size-fits-all" dosing by using the individual’s unique dose-response profile (N-of-1) to dynamically optimise BP control4. Purpose To evaluate the safety and clinical feasibility of integrating CURATE.AI, an individualised, AI-assisted clinical decision support system, for antihypertensive dose titration in routine outpatient care. Methods In this open-label, randomised trial, 45 participants with primary hypertension were randomised (1:1:1) into control (standard care; CT), home BP monitoring (HBPM), or HBPM with AI-assisted titration (CURATE.AI) arm. CURATE.AI generated individualised quadratic dose response profiles based on dose-BP pairs, guiding physician dose recommendations. Primary endpoints assessed logistical and scientific feasibility with prespecified “traffic light” performance targets5. Exploratory secondary endpoints included mean change in office BP and time to target BP. Results 42 participants were included in the final analysis (CT n=12, HBPM n=15, CURATE.AI n=15). No adverse events or serious adverse events occurred. Clinical feasibility was high, with strong adherence to home BP monitoring (82%; green) and drug dosing (99%; green), and physician acceptability of CURATE.AI recommendations (64%; yellow). However, rapid achievement of BP targets in all 3 randomised groups meant that CURATE.AI successfully generated dose-response profiles for only 33% of participants (yellow), increasing to 73% (green) when including participants already within the target BP range. Although cumulative CURATE.AI guided doses differed substantially (≥10%) from standard care in 100% of participants (green), we observed no significant between group differences in 1-month office BP (SBP, ANCOVA: F(2,38)=1.84, p=0.173; DBP, F(2,38)=2.73, p=0.078). Time to target BP between HBPM and CURATE.AI did not differ significantly (SBP, log-rank p=0.889; HR=0.92, 95% CI 0.26–3.24; DBP, log-rank p=0.181; HR=0.47, 95% CI 0.14–1.55). Time in therapeutic range for home BP also did not differ significantly. Conclusions This proof-of-concept trial demonstrates that AI-assisted titration of antihypertensive medication is both safe and feasible for collaborative clinical use. Although BP outcomes were not superior to standard care, the study was not powered for efficacy. The pilot findings highlight areas for refinement of the study design and support the feasibility of conducting a larger randomised controlled trial to evaluate efficacy and cardiovascular outcomes.Curate.AI guided titrationoutcomes
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Wang et al. (2026) conducted an RCT in Primary hypertension (n=45). Home BP monitoring with AI-assisted titration (CURATE.AI) vs. Standard care (CT) or home BP monitoring alone (HBPM) was evaluated on Logistical and scientific feasibility (home BP monitoring adherence, drug dosing adherence, physician acceptability, and dose-response profile generation). AI-assisted antihypertensive titration (CURATE.AI) was safe and feasible (82% HBPM and 99% dosing adherence), with no significant 1-month SBP difference vs standard care (p=0.173).
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