Digital Cardiovascular Twins, AI Agents, and Sensor Data: A Narrative Review from System Architecture to Proactive...

Area of Science:

• Cardiovascular medicine and digital health.
• Artificial intelligence in healthcare.
• Biomedical engineering and sensor technology.

Background:

• Cardiovascular disease (CVD) is a leading cause of mortality, with current treatments often reactive, intervening only after significant tissue damage.
• Existing diagnostic methods for ischemia, arrhythmias, and remodeling are episodic and detect pathologies late, limiting therapeutic effectiveness.
• The need for continuous, predictive monitoring in cardiology is critical to shift towards preventive care.

Purpose of the Study:

• To review innovative diagnostic technologies for cardiovascular disease (CVD).
• To examine the integration of digital cardiovascular twins, artificial intelligence (AI), and multi-modal data streams for predictive cardiology.
• To explore the potential of AI-driven systems for early detection and personalized prevention of CVD.

Main Methods:

• A narrative review of 183 studies (2016-2025) from major scientific databases (PubMed, Scopus, etc.).
• Analysis of digital cardiovascular twin architectures, incorporating data from wearable IoT devices (ECG, PPG), clinical records, biomarkers, and genetic markers.
• Examination of AI techniques including machine learning, deep learning, graph/transformer networks, generative AI, medical LLMs, and autonomous agents for data interpretation and decision support.

Main Results:

• Digital cardiovascular twins, powered by AI and multi-modal data, enable early detection of silent pathologies weeks before clinical manifestation.
• A four-layered architecture comprising sensors, hybrid analytics, AI-driven decision support, and prospective simulations facilitates continuous monitoring and actionable recommendations.
• The integration of hemodynamic models with advanced AI techniques effectively manages uncertainty and creates prognostic models for personalized interventions.

Conclusions:

• This multi-layered approach transforms continuous cardiovascular data into actionable insights, enabling predictive and preventive care.
• The developed framework supports the creation of software-as-a-medical-device ecosystems and informs regulatory guidance for trustworthy AI in cardiovascular medicine.
• The shift from reactive treatment to proactive, personalized prevention holds significant promise for reducing cardiovascular mortality.