An artificial intelligence model has been developed to provide individualised treatment advice for patients with atrial fibrillation (AF). The AI assists clinicians in determining whether or not to prescribe anticoagulants to prevent stroke, which is the current standard treatment. The AI co-pilot uses machine learning techniques and integrates guidelines from organisations such as the American College of Cardiology, American Heart Association, and the European Society of Cardiology. The AI demonstrated a high degree of accuracy with a precision of 92%, sensitivity of 89%, and specificity of 91%.
AI has emerged as a transformative tool in AF care, leveraging machine learning and deep learning algorithms to enhance diagnostic accuracy, improve risk prediction, and guide therapeutic interventions. AI-driven predictive models integrate electronic health records and multimodal physiological data to refine AF risk stratification, stroke prediction, and anticoagulation decision making. In treatment, AI is revolutionising individualised therapy and optimising anticoagulation management and catheter ablation strategies.
AI-enhanced electroanatomic mapping and real-time procedural guidance hold promise for improving ablation success rates and reducing AF recurrence. The technology has the potential to support clinical decision-making by providing personalised, data-driven recommendations for oral anticoagulation in patients with atrial fibrillation.
