Artificial Intelligence for Cardiovascular Health Behavior Change: A Narrative Review of Patient-Facing Tools

Cardiovascular disease (CVD) is the leading cause of death in the United States and disproportionately affects racial/ethnic minoritized populations, low-income communities, older adults, and rural residents. Behavioral interventions can reduce cardiovascular risk, yet traditional programs face scalability barriers, including high costs and limited reach. This narrative review synthesizes evidence on the efficacy and equity of patient-directed AI tools that support health education and behavior change for cardiovascular health. Using PRISMA-ScR elements, we searched 10 databases (January 2010 to December 2025), yielding 19 empirical studies evaluating Generation 2.0 AI systems aligned with the American Heart Association’s Life’s Essential 8 (LE8) framework. Of these, five were randomized controlled trials (RCTs), and 14 were nonrandomized (quasi-experimental, observational, or early-stage) studies. AI-enabled interventions such as conversational agents, machine-learning models, and reinforcement-learning systems addressed hypertension, diabetes, heart failure, and related conditions. Among the RCTs, one demonstrated superiority of an AI-powered program over usual care (AI-delivered Diabetes Prevention Program), whereas the remaining four reported outcomes equivalent or non-inferior to standard behavioral care. Effects were most apparent in high-intensity interventions that paired machine learning–based personalization with structured behavioral curricula, although the multi-component nature of these programs limits inference about AI-specific contributions. Across nonrandomized studies, greater engagement intensity was consistently associated with improved cardiometabolic outcomes, although causal attribution to AI-specific mechanisms remains limited. Most studies had small samples, short follow-up, and limited reporting on safety and equity. Technology-intensive interventions were concentrated in higher-resourced settings, and populations with limited health literacy showed reduced engagement. Overall, evidence supports cautious optimism that AI can meaningfully scale personalized cardiovascular prevention; however, realizing that equitable and clinically meaningful impact will require rigorous trials with longer follow-up, explicit behavioral science integration, safety-conscious governance, and equity-centered strategies addressing digital determinants of health.