What question did this study set out to answer?

This review aims to explore how artificial intelligence can transform diagnosis and management in cardiology.

February 21, 2026Therapeutic Advances in Cardiovascular Disease0 citationsOpen Access

Artificial intelligence for cardiology: from diagnosis to management

VNVasanthrie NaidooDurban University of Technology LMLavanya MadamshettyDurban University of Technology SKSuresh babu Naidu KrishnaDurban University of Technology

Key Result

AI enhances diagnostic precision, management, and procedural outcomes in cardiology including ablation, supported by interdisciplinary collaboration across 142 studies.

Key Points

This review aims to explore how artificial intelligence can transform diagnosis and management in cardiology.
Conducted a scoping review of 142 studies
Examined AI applications in diagnostics and predictive analytics
Evaluated the role of interdisciplinary teams in implementing AI
Identified challenges and proposed strategies for AI integration
AI enhances diagnostic precision in cardiology
Improves patient outcomes in procedures like cardiac ablation
Facilitates predictive analytics for better risk assessments
Highlights the need for interdisciplinary collaboration for successful AI implementation

Structured PICO

Population

142 studies evaluating artificial intelligence and machine learning applications in cardiology, particularly cardiac electrophysiology

Intervention

Artificial intelligence and machine learning technologies

Artificial intelligence and machine learning have broad applications in cardiology and electrophysiology for diagnosis and management, though successful clinical integration requires addressing ethical considerations and adoption barriers.

Main Result

Absolute Event Rate: 0% vs 0%

Abstract

Artificial intelligence (AI) and machine learning are rapidly transforming cardiac electrophysiology, offering new avenues for diagnosing, managing, and treating cardiac arrhythmias. These technologies leverage diverse data sources, including clinical records, imaging, and electrical waveforms, to support decision-making and optimize outcomes, particularly in procedures such as cardiac ablation. This scoping review explores the evolving role of AI in cardiology, emphasizing its applications in diagnostics, predictive analytics, and procedural innovations. It also examines the collaborative dynamics of interdisciplinary teams, highlighting how professionals, such as electrophysiologists, computer scientists, clinicians, nurses, perfusionists, and technologists, contribute to identifying and solving key challenges in the field. The integration of AI into cardiology is not only enhancing diagnostic precision and patient outcomes but also streamlining healthcare delivery. As technological capabilities expand, AI is poised to play an increasingly central role in preventive cardiology, enabling more accurate risk assessments, earlier interventions, and the promotion of healthier lifestyles. However, the successful implementation of AI requires thoughtful coordination across disciplines and a clear understanding of its limitations and ethical considerations. This review underscores the importance of fostering interdisciplinary collaboration and aligning AI innovations with clinical needs. It also identifies barriers to adoption and proposes strategies for integrating AI tools into routine practice. Ultimately, the findings aim to guide stakeholders, including researchers, clinicians, and policymakers, in advancing the development and application of AI systems in cardiology. By doing so, the healthcare community can move toward reducing the global burden of cardiovascular disease and improving population health. The insights presented here, after a review of 142 studies, offer a roadmap for future research and clinical integration, ensuring that AI continues to serve as a catalyst for innovation and excellence in cardiac care.

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