Key result
AI and machine learning show promise to enhance AF diagnosis, treatment, and monitoring.
Why the study?
Atrial fibrillation management presents major challenges due to its complexity and diverse patient profiles, making the integration of artificial intelligence and machine learning a promising approach to improve care.
Artificial intelligence and machine learning offer promising applications across the spectrum of atrial fibrillation management, including diagnosis, risk prediction, treatment selection, and patient monitoring.
Supports exploratory AI use in AF care; leaves open the need for prospective validation before practice change.
Atrial fibrillation (AF) is the most common cardiac arrhythmia encountered in clinical practice, affecting millions of individuals worldwide. The management of AF presents significant challenges due to its complex nature and diverse patient profiles. In recent years, the integration of artificial intelligence (AI) and machine learning technologies has emerged as a promising approach to enhance various aspects of AF management. This comprehensive review aims to explore the evolving role of AI in AF management, with a focus on its potential applications in diagnosis, risk prediction, treatment selection, and patient monitoring. Real academic references were employed to provide evidence-based insights into the impact of AI on AF management.
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Fahimi et al. (2023) conducted a review in Atrial fibrillation. Artificial intelligence and machine learning technologies was evaluated. Artificial intelligence and machine learning technologies show promising potential to enhance atrial fibrillation management across diagnosis, risk prediction, treatment selection, and monitoring.
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