Artificial Intelligence Across the Continuum of Atrial Fibrillation Screening, Diagnosis, and Treatment

The review aims to help clinicians understand the current landscape of artificial intelligence (AI) tools for atrial fibrillation (AF). It explores the readiness of these tools for clinical use and identifies areas requiring further research.

AI has increasingly played a crucial role in supporting AF care across the continuum of screening, diagnosis, and management. First, AI optimizes AF screening by refining patient selection through AF risk prediction. While most of these models have undergone substantial validation, their performance and feasibility for large-scale implementation vary. Secondly, AI has demonstrated its effectiveness in enhancing the diagnostic capabilities of ECGs, mobile cardiac monitors, and wearables by providing accurate, real-time detection. Thirdly, in the management of AF patients, emerging causal machine learning models have been developed to personalize treatment choices, yet rigorous evaluation in routine clinical practice remains pending. Within these three domains, AI also holds promise for stroke risk stratification, clinical decision support, patient education, and enhancing patients’ and clinicians’ adherence to guideline-recommended care, but such areas are either underdeveloped or have not yet shown a significant impact.

AI holds promise in supporting the screening, diagnosis, and management of AF. AI has been most reliably applied to streamlining AF diagnosis and is ready for clinical use in selecting screening populations. However, there is still significant progress required before it can be used to tailor treatment decisions for individual patients.