Artificial Intelligence in Cardiac Electrophysiology Ethics, Explainability, Equity, and the Road Ahead.
TL;DR
The ethical foundations of AI in electrophysiology are examined, focusing on dataset bias, fairness, explainability, and accountability, and future horizons including digital twins, multimodal risk engines, and real time procedural guidance systems are highlighted.
Abstract
Artificial intelligence (AI) is rapidly transforming cardiac electrophysiology (EP) by enabling automated interpretation of electrocardiograms, intracardiac electrograms, wearable sensor data, and imaging modalities. These technologies promise improved diagnostic accuracy, procedural efficiency, and personalized risk stratification across a wide spectrum of arrhythmia care. However, the integration of AI into clinical EP raises important ethical, practical, and regulatory concerns. Many current models function as opaque systems with limited explainability, while training datasets often lack adequate representation across sex, race, geography, and socioeconomic status. These limitations raise the risk of biased performance, reduced generalizability, and erosion of clinician trust. Furthermore, cost, infrastructure requirements, and regulatory complexity threaten equitable global adoption. This review examines the ethical foundations of AI in electrophysiology, focusing on dataset bias, fairness, explainability, and accountability. We discuss the clinical consequences of black box models, outline strategies to improve transparency, and explore emerging regulatory and legal frameworks. Finally, we highlight future horizons including digital twins, multimodal risk engines, and real time procedural guidance systems. Responsible deployment of AI in EP will require rigorous validation, continuous monitoring, clinician oversight, and intentional efforts to ensure equitable access across diverse populations.
