From Algorithm Development to Clinical Implementation: A Systematic Review of Artificial Intelligence in Cardiovascular Medicine
TL;DR
Although AI demonstrated promising diagnostic and prognostic performance across multiple cardiovascular conditions, most applications remain at the validation stage, and future research should prioritise implementation science, pragmatic evaluation, and real-world evidence to facilitate routine adoption and maximise patient benefit and healthcare value.
Abstract
Artificial intelligence (AI) is transforming cardiovascular medicine through applications in disease detection, diagnosis, risk prediction, and clinical decision support. However, the clinical implementation of these technologies remains poorly characterised. This systematic review evaluated the current landscape of AI applications in cardiovascular medicine, focusing on implementation maturity and clinical translation. The review followed PRISMA guidelines and a prospectively registered PROSPERO protocol. Data extraction included study characteristics, cardiovascular domain, AI methodology, clinical application, validation strategy and implementation maturity, assessed using a predefined five-level framework. AI methodologies were classified as conventional machine learning, deep learning, hybrid ML/deep learning, multimodal AI, or large language models/generative AI. Seventy-four studies met the eligibility criteria. Conventional machine learning was the most frequently used methodology (47.3%), followed by deep learning (41.9%), whereas multimodal AI (5.4%), hybrid ML/deep learning (2.7%), and large language models/generative AI (2.7%) were uncommon. Applications focused mainly on screening and early detection (31.1%), risk stratification and prognosis (25.7%), treatment planning (16.2%), diagnosis (13.5%), and monitoring (12.2%). Most studies reached implementation maturity Level 3 (clinical validation, 47.3%) or Level 2 (technical validation, 35.1%), while only 13.5% achieved routine clinical implementation (Level 5) and 4.1% reached clinical deployment (Level 4). Although AI demonstrated promising diagnostic and prognostic performance across multiple cardiovascular conditions, most applications remain at the validation stage. Future research should prioritise implementation science, pragmatic evaluation, and real-world evidence to facilitate routine adoption and maximise patient benefit and healthcare value.