Artificial intelligence across the cardiovascular diagnostic pathway: a case-based narrative review

Artificial intelligence (AI) is increasingly embedded across cardiovascular care, serving as a powerful adjunct for symptom assessment, bedside examination, electrocardiography, and multimodality imaging interpretation. We aim to thoroughly review current evidence for AI applications across the cardiovascular diagnostic pathway and to highlight key considerations for clinical integration.

We performed a narrative review of clinical trials and observational studies retrieved from MEDLINE/PubMed, Embase, and Google Scholar (January 1^st 2000-July 10^th 2025), limited to publications in English, using AI- and cardiovascular diagnostic specific search terms. Regulatory resources [e.g., U.S. Food and Drug Administration (FDA) clearance databases and publicly available summaries] were also reviewed to identify cardiovascular AI software with regulatory authorization.

Across diagnostic domains, AI has demonstrated potential to improve diagnostic performance and workflow efficiency. Large language models and other AI systems can support structured history-taking, triage, and automated clinical documentation. Digital stethoscope and phonocardiography algorithms enable scalable screening for murmurs and valvular disease with a higher sensitivity for murmur detection compared with conventional auscultation. Electrocardiography-based AI models have been reported for rapid detection of arrhythmias, ischemia, and heart failure with reduced ejection fraction (EF). In echocardiography, AI enables automated view classification, chamber quantification, EF estimation, and valve assessment, while substantially reducing acquisition and processing time. Advanced imaging tools support coronary computed tomography (CT) angiography plaque characterization, calcium scoring, and CT-derived fractional flow reserve (FFR), as well as cardiac magnetic resonance segmentation and scar/late gadolinium enhancement (LGE) quantification. However, much of the evidence remains retrospective with heterogeneous endpoints, and outcome-improving, prospective, real-world integration studies remain limited.

Future work should prioritize multicenter prospective validation and implementation studies that address model generalizability, quality control, bias, data drift, and governance. Multimodal, workflow-embedded AI systems that fuse clinical and imaging signals may ultimately enable individualized risk prediction and improve access and cardiovascular outcomes.