From assistance to autonomy: AI agent systems in cardiovascular medicine—a review of paradigms, architectures, and clinical translation

Abstract

Background Cardiovascular medicine faces persistent implementation gaps driven by workforce shortages, fragmented data systems, and the cognitive burden of complex clinical decision-making—structural constraints that limit the delivery of guideline-directed care. Artificial intelligence (AI) is transitioning from isolated predictive models toward autonomous agent systems capable of perceiving, reasoning, and acting in clinical environments, offering a potential pathway to address these challenges. Objective This review synthesizes current evidence on AI agent systems in cardiovascular medicine across four interconnected dimensions: technical paradigms enabling agentic functionality (multi-agent systems, digital twins, multimodal integration), emerging clinical applications across the cardiovascular continuum, and governance frameworks essential for responsible translation. Methods We conducted a narrative review of peer-reviewed literature published between January 2020 and March 2026, drawing from PubMed, Web of Science, IEEE Xplore, and Scopus databases. Search terms included combinations of “artificial intelligence”, “AI agents”, “autonomous agents”, “multi-agent systems”, “large language models”, “cardiovascular diseases”, “heart failure”, “digital twins”, and “clinical decision support”. Emphasis was placed on high-quality original research, systematic reviews, and position papers from major cardiovascular societies, with particular attention to developments from 2024 to 2026. Results Agentic AI systems should function as augmented intelligence—enhancing rather than replacing clinical judgment—to close implementation gaps in cardiovascular care. Multi-agent architectures, digital cardiovascular twins, and multimodal integration are emerging as core technical paradigms. The ClinNoteAgents system demonstrates high extraction fidelity (conditional accuracy ≥90%) for clinical variables while achieving 60%–90% text reduction. Digital twin applications span therapy planning, risk prediction, and monitoring, with 69% relying on mechanistic models and 76% utilizing imaging data for personalization. Heart failure has emerged as a paradigmatic use case, driven by structural workforce gaps and the ARPA-H ADVOCATE initiative launched in January 2026. The C.A.R.D.I.O. framework (Clinical validation, Auditability, Risk stratification, Data privacy, Integration, Ongoing vigilance) and the CURACO framework (Clinical safety, Understanding, Research-informed care, Authentic patient-centred approaches, Conscientious ethics, Optimised technology) provide governance structures for responsible deployment. A rapid systematic review of 13 studies including 22,641 participants found that 85% of AI interventions improved cardiovascular outcomes, with mortality reductions of 0.8%–12% and major adverse cardiovascular event reductions of 4%–12%. Conclusion Cardiovascular medicine stands at an inflection point. The transition from assistance to autonomy requires rigorous fit-for-purpose evaluation, transparent interpretability mechanisms, and robust governance frameworks. Agentic AI systems should function as augmented intelligence—enhancing rather than replacing clinical judgment—to close implementation gaps in cardiovascular care.