A clinically grounded taxonomy and systematic review of artificial intelligence for cardiovascular diagnosis: From...

Cardiovascular diseases (CVDs) remain the leading cause of mortality worldwide, creating an urgent need for accurate, trustworthy, and clinically deployable artificial intelligence (AI) systems capable of supporting complex diagnostic decision-making. Although AI has advanced considerably in cardiovascular diagnosis, existing evidence remains fragmented across algorithms, data modalities, and isolated application domains, limiting a comprehensive understanding of clinically integrated AI systems. This study presents a PRISMA 2020-guided systematic review and proposes a clinically grounded six-layer taxonomy that organizes cardiovascular AI according to diagnostic objectives, data modalities, modeling paradigms, data integration complexity, interpretability and trustworthiness, and deployment maturity. A systematic search of PubMed, Scopus, Web of Science, IEEE Xplore, and ScienceDirect identified 226 records, of which 76 primary empirical studies met the predefined eligibility criteria and were included in the comparative evidence synthesis. The review demonstrates the evolution of cardiovascular AI from conventional machine learning applied to structured clinical data toward deep learning for physiological signals and medical imaging, followed by multimodal AI systems integrating heterogeneous clinical information. Comparative synthesis across the proposed taxonomy highlights substantial progress in predictive performance while revealing persistent challenges related to external validation, dataset representativeness, workflow integration, explainability, privacy, governance, and prospective clinical deployment. The review further distinguishes clinically validated technologies from emerging paradigms, including federated learning, foundation models, and agentic AI. Overall, the proposed taxonomy provides a unified framework for organizing contemporary cardiovascular AI research and offers a practical roadmap for evaluating the maturity, trustworthiness, and clinical readiness of next-generation intelligent diagnostic systems.