Explainable Artificial Intelligence for Clinical Decision Support in Cardiac Rhythm Device Programming
Shaik Balkhis Banu, R Murugesan · 2025
The increasing sophistication of cardiac rhythm management devices such as pacemakers and implantable cardioverter-defibrillators has created a demand for intelligent, adaptive programming that can respond to dynamic patient conditions. Artificial intelligence offers powerful tools for detecting arrhythmias, predicting adverse events, and optimizing device parameters using complex physiological data streams. Yet the opaque nature of many AI models poses a barrier to clinical trust and safe deployment in life-critical applications. Explainable artificial intelligence (XAI) addresses this challenge by providing transparent, interpretable insights into how AI models generate recommendations for device programming. This chapter explores the foundational principles, data requirements, machine learning architectures, and explainability techniques relevant to cardiac electrophysiology. It discusses the design of XAI-integrated clinical decision support systems that deliver actionable explanations through intuitive user interfaces while preserving clinician oversight. Topics include human-in-the-loop interaction, trust calibration, continuous learning, and regulatory considerations for deploying XAI-enabled cardiac devices. By bridging advanced computational intelligence with human-centered transparency, XAI empowers electrophysiologists to validate, refine, and trust automated programming adjustments. This integration holds promise for safer, more personalized, and clinically acceptable cardiac rhythm management in an era of increasingly complex patient needs and device capabilities.