Artificial intelligence and remote monitoring of cardiac devices: A narrative review
- 1Centre for Heart Rhythm Disorders, Adelaide University and the Royal Adelaide Hospital, Adelaide, South Australia, Australia
Abstract
Remote monitoring of Cardiac Implantable Electronic Devices (CIEDs) has evolved significantly from early trans-telephonic transmissions to now becoming a standard of care that improves survival and outcomes. However, the resulting exponential growth in data has resulted in alert fatigue and information overload, creating a need for advanced data interpretation. This narrative review examines the transformative role of Artificial Intelligence (AI) in reshaping remote monitoring and cardiac device management into an active, predictive, and personalised system. We examine the role of AI across all cardiac implantable electronic devices, including implantable loop recorders (ILRs), pacemakers, implantable cardioverter-defibrillators (ICDs) and cardiac resynchronisation therapy (CRT) devices. We address four questions: (1) the scale and nature of the remote monitoring burden; (2) the AI tools in current clinical use, particularly for ILRs, where alert burden is greatest; (3) AI applications evaluated but not yet widely implemented, including prediction of ventricular arrhythmias, heart failure events and device failure; and (4) future directions, including adaptive device programming and digital twins. While AI demonstrates significant potential to enhance clinical decision-making and patient outcomes, its widespread adoption remains constrained by barriers regarding data quality, algorithm transparency, regulatory compliance, and privacy protections. Realising the full potential of AI in electrophysiology will require sustained collaboration between clinical and technical stakeholders to ensure these tools are deployed effectively and safely.
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BibTeX
@article{shahmohamadi2026artificial,
title = {{Artificial intelligence and remote monitoring of cardiac devices: A narrative review}},
author = {Elnaz Shahmohamadi and Shaun C. Evans and Melissa E. Middeldorp and Prashanthan Sanders},
journal = {Indian Pacing and Electrophysiology Journal},
year = {2026},
month = oct,
doi = {10.1016/j.ipej.2026.10.003},
url = {https://doi.org/10.1016/j.ipej.2026.10.003},
note = {CC BY-NC-ND 4.0},
}
RIS
TY - JOUR
TI - Artificial intelligence and remote monitoring of cardiac devices: A narrative review
AU - Elnaz Shahmohamadi
AU - Shaun C. Evans
AU - Melissa E. Middeldorp
AU - Prashanthan Sanders
PY - 2026
DA - 2026/10/05
JO - Indian Pacing and Electrophysiology Journal
DO - 10.1016/j.ipej.2026.10.003
UR - https://doi.org/10.1016/j.ipej.2026.10.003
AB - Remote monitoring of Cardiac Implantable Electronic Devices (CIEDs) has evolved significantly from early trans-telephonic transmissions to now becoming a standard of care that improves survival and outcomes. However, the resulting exponential growth in data has resulted in alert fatigue and information overload, creating a need for advanced data interpretation. This narrative review examines the transformative role of Artificial Intelligence (AI) in reshaping remote monitoring and cardiac device management into an active, predictive, and personalised system. We examine the role of AI across all cardiac implantable electronic devices, including implantable loop recorders (ILRs), pacemakers, implantable cardioverter-defibrillators (ICDs) and cardiac resynchronisation therapy (CRT) devices. We address four questions: (1) the scale and nature of the remote monitoring burden; (2) the AI tools in current clinical use, particularly for ILRs, where alert burden is greatest; (3) AI applications evaluated but not yet widely implemented, including prediction of ventricular arrhythmias, heart failure events and device failure; and (4) future directions, including adaptive device programming and digital twins. While AI demonstrates significant potential to enhance clinical decision-making and patient outcomes, its widespread adoption remains constrained by barriers regarding data quality, algorithm transparency, regulatory compliance, and privacy protections. Realising the full potential of AI in electrophysiology will require sustained collaboration between clinical and technical stakeholders to ensure these tools are deployed effectively and safely.
SN - 0972-6292
ER -