AI-powered cardiovascular care system demonstrated potential to improve cardiovascular health literacy and early risk detection, according to a new study.
Cardiovascular diseases remain a major global health challenge, with barriers such as low health literacy, misinformation, language differences, and fragmented patient engagement continuing to affect prevention and long-term management.
To address these issues, researchers developed a comprehensive digital platform that integrates a Digital Human Agent, artificial intelligence (AI), natural language processing, and guideline-based ASCVD risk assessment within a single system.
Accessible via both mobile and web platforms, the system uses bilingual English and Arabic voice or text interactions to provide personalised cardiovascular education and risk-based recommendations.
The AI assistant was designed to simplify medical terminology and deliver tailored information according to an individual’s cardiovascular risk level.
A corresponding clinician dashboard allows cardiologists to monitor patient data, review risk trends, and communicate directly with users in real time.
The platform incorporates a validated ASCVD risk assessment model aligned with established cardiovascular prevention guidelines.
Users provide information including age, cholesterol levels, blood pressure, smoking status, and diabetes status.
The system then generates a 10-year ASCVD risk estimate and categorises individuals into low, borderline, intermediate, or high-risk groups.
Researchers verified the accuracy of the ASCVD calculations against established reference cases, with all tested scenarios matching published risk estimates.
Once risk scores are generated, the AI converts complex clinical information into accessible explanations and personalised recommendations intended to support patient understanding and engagement.
The Digital Human Agent delivers these recommendations through conversational interactions using both spoken and written responses.
Risk guidance is adapted according to the user’s preferred language and calculated cardiovascular risk category.
Initial evaluation focused on software validation, expert review, and functional testing using synthetic patient data rather than clinical outcomes.
Feedback from healthcare professionals highlighted demand for simplified medical language, interactive educational tools, and personalised risk information.
Surveys also demonstrated strong public interest in mobile health applications and tailored cardiovascular guidance.
According to the researchers, the system may help strengthen digital health literacy, improve patient engagement, and support closer collaboration between patients and cardiologists.
However, they acknowledged that formal clinical validation, assessment of health outcomes, and real-world testing remain necessary before broader implementation.
Future work will focus on longitudinal risk tracking, predictive disease modelling, and controlled evaluations of user engagement and comprehension.
Reference
Al-Rajab M et al. My Heart: a personalized interactive cardiovascular care system using artificial intelligence. Sci Rep. 2026;DOI:10.1038/s41598-026-68939-z.
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