Loading
Artificial Intelligence in Healthcare and Education · Journal article
Medical Sciences · July 25, 2026
A consensus or society position rather than new primary data.
This is a narrative review that traces the historical and clinical evolution of artificial intelligence in cardiovascular medicine from early automated ECG interpretation to contemporary multimodal applications. It synthesizes technological developments, current clinical applications, and translational challenges but does not report original research findings, effect sizes, or quantified clinical outcomes. The review provides contextual guidance on the landscape of AI in cardiovascular care rather than evidence for or against specific clinical interventions.
Narrative review. Cardiovascular medicine practitioners, researchers, and digital health system stakeholders; review does not study a specific patient population..
AI has evolved from rule-based ECG interpretation systems to sophisticated deep learning models capable of analysing complex cardiovascular signals and imaging data Multimodal AI models now integrate heterogeneous data sources including imaging, physiological signals, clinical records, and genomic information AI applications span multiple cardiovascular domains: electrocardiographic and electrophysiological analysis, imaging, surgical planning, and multimodal risk prediction
Safety was not reported in the material analysed. Check the source before drawing any conclusion about harm.
Clinicians should use this review to understand the current landscape and trajectory of AI applications in cardiovascular care, including opportunities for early disease detection, risk stratification, prognostic modelling, and personalised care, whilst remaining aware that this is a contextual synthesis rather than a source of efficacy evidence for specific clinical tools.
Narrative review synthesizing the evolution and current applications of AI in cardiovascular medicine, providing clinical context and overview rather than reporting original research findings with quantified evidence.
As stated by the source record.
Clinicians should use this review to understand the current landscape and trajectory of AI applications in cardiovascular care, including opportunities for early disease detection, risk stratification, prognostic modelling, and personalised care, whilst remaining aware that this is a contextual synthesis rather than a source of efficacy evidence for specific clinical tools.
Graded across the dimensions that decide whether you should act, each from what the source actually supports. There is no single score, and where a dimension was not assessed it says so.
What is missing. This record has no reported figures. That is a gap in the analysis, not a judgement about the study.
Artificial intelligence (AI) is rapidly transforming cardiovascular medicine, driven by the increasing availability of large-scale clinical data and advances in machine learning. Early computational applications in cardiology were primarily limited to rule-based electrocardiogram interpretation systems. Over time, these approaches have evolved into sophisticated deep learning models capable of analysing complex cardiovascular signals and imaging data. In parallel with the broader development of digital health technologies, including wearable devices, electronic health records, and remote monitoring systems, AI applications have expanded across multiple domains of cardiovascular care. These now include electrocardiographic (ECG) and electrophysiological analysis, cardiovascular imaging, surgical planning, and multimodal risk prediction. More recently, multimodal AI models have emerged that integrate heterogeneous data sources such as imaging, physiological signals, clinical records, and genomic information, enabling more comprehensive characterisation of cardiovascular disease. Beyond diagnostic applications, AI is increasingly influencing system-level aspects of cardiovascular medicine, including clinical decision support, workflow optimisation, medical education, and clinical trial design. This narrative review traces the historical and clinical evolution of artificial intelligence in cardiovascular medicine from early automated ECG interpretation systems to contemporary multimodal and system-level applications. It highlights key technological developments, current clinical applications, translational challenges, and the emerging role of AI within digital cardiovascular health ecosystems, with particular emphasis on early disease detection, risk stratification, prognostic modelling, and personalised cardiovascular care.
Taken from the source record, never inferred. Follow any of these and new work involving them reaches your briefing.