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…
How strong is the evidence?
Evidence Hub reads this as a systematic review, done in people. Its evidence level is 1 of 5 (Highest · pooled evidence). Pooled evidence from several studies sits at the top of the evidence pyramid. Reported size: 22,641 participants or records.
Read the paper
Related studies
- Active Learning during Federated Learning for Object Detection
- Fairness-aware federated learning
- Decentralized Federated Learning
- Building a Personally Identifiable Information Recognizer in a Privacy Preserved Manner Using Automated Annotation and Federated Learning
- Preservation of Patient Level Privacy: Federated Classification and Calibration Models
- Efficient explainable federated learning
Go deeper in Evidence Hub
Evidence Hub adds a study data sheet (design, sample size, evidence level, key numbers), an AI summary and a chat grounded in the paper's text.