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Understanding Artificial Intelligence (AI) for the Electrophysiologist.

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Artificial intelligence (AI) offers many applications in clinical electrophysiology, but its adoption requires careful evaluation. This review guides electrophysiologists in understanding AI tools for responsible clinical integration and improved patient care.

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Area of Science:

  • Clinical electrophysiology
  • Artificial intelligence applications in medicine

Background:

  • Artificial intelligence (AI) is increasingly integrated into clinical electrophysiology, with applications in ECG interpretation, arrhythmia detection, and workflow support.
  • Variability in AI methodology, validation, and integration creates uncertainty for clinical use.

Purpose of the Study:

  • To provide electrophysiologists with a practical guide for evaluating and adopting AI tools.
  • To support informed interpretation and responsible clinical integration of AI in electrophysiology.

Main Methods:

  • Review of AI evolution from rule-based systems to machine learning, deep learning, and generative AI.
  • Examination of core concepts: data provenance, labeling, validation, analytical performance vs. clinical utility.
  • Analysis of common AI failure modes (bias, overfitting, interpretability) and regulatory evaluation processes.

Main Results:

  • AI tools span automated interpretation, arrhythmia detection, risk stratification, and procedural planning.
  • Key considerations include data quality, validation rigor, and distinguishing analytical performance from clinical utility.
  • Regulatory clearance has specific limitations, and clinicians retain responsibility for oversight.

Conclusions:

  • Electrophysiologists need a framework to interpret AI evidence and guide its evaluation and adoption.
  • Responsible AI integration requires understanding methodological rigor, potential failure modes, and regulatory landscapes.
  • Future AI directions include personalized modeling, decision support, and enhancing efficiency and access to care.