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.