HRS Scientific Statement on Artificial Intelligence Integration Framework into Clinical Electrophysiology Workflows

April 20, 2026—The Heart Rhythm Society (HRS) released HRS Scientific Statement on Artificial Intelligence Integration Framework into Clinical Electrophysiology Workflows, providing a comprehensive framework to guide the responsible adoption and integration of artificial intelligence (AI) and digital health technologies (DHTs) into electrophysiology (EP) practice.

As AI continues to rapidly transform healthcare, this scientific statement addresses a critical need: helping clinicians understand where, how, and when AI can be safely and effectively integrated into clinical workflows. The document emphasizes that while AI offers powerful opportunities to improve diagnosis, risk prediction, workflow efficiency, and patient outcomes, its implementation must be thoughtful, evidence-based, and continuously evaluated.

The statement outlines a structured, lifecycle-based approach to AI integration in EP, spanning three core domains: current and emerging applications, readiness for adoption, and ongoing evaluation of safety and effectiveness. It highlights how AI can support clinical care through automation (e.g., ECG interpretation and remote monitoring), data organization (e.g., clinical documentation and workflow optimization), and predictive analytics (e.g., arrhythmia detection and risk stratification).

Key themes include:

• The importance of scientific validation and regulatory oversight prior to clinical adoption
• Integration of AI into existing workflows using implementation science and user-centered design
• The need for data interoperability across devices, platforms, and electronic health records
• Establishing governance structures for monitoring performance, safety, and unintended consequences
• Continuous lifecycle evaluation, including recalibration, retraining, or cessation when necessary

The document also addresses practical considerations for clinicians and health systems, including infrastructure requirements, data security, reimbursement challenges, and workflow integration. It underscores that successful implementation depends not only on technical performance but also on clinician trust, usability, and alignment with clinical needs.

Importantly, the statement highlights the ethical and societal implications of AI in healthcare, emphasizing principles such as transparency, accountability, bias mitigation, patient privacy, and health equity. It reinforces that AI should augment, not replace, clinical judgment, with clinicians maintaining ultimate responsibility for patient care.

Overall, this scientific statement provides a practical and forward-looking roadmap for integrating AI into EP workflows, supporting safe innovation while promoting high-quality, equitable, and patient-centered care.