# Understanding Artificial Intelligence (AI) for the Electrophysiologist

## TL;DR

This paper explains how AI is being used in heart rhythm care and provides guidelines for doctors to evaluate and use these tools responsibly.

## Contribution

The paper offers a practical framework for electrophysiologists to assess and adopt AI tools in clinical settings.

## Key findings

- AI performance in electrophysiology depends on factors like model architecture and data quality.
- Common AI failures include biased datasets and misaligned clinical workflows.
- FDA clearance does not guarantee clinical effectiveness or generalizability of AI tools.

## Abstract

Artificial intelligence (AI) is increasingly incorporated into clinical electrophysiology, Applications now span automated ECG interpretation, arrhythmia detection, risk stratification, procedural planning, and workflow support. At the same time, variability in methodological rigor, validation standards, and clinical integration has led to uncertainty regarding how these tools should be interpreted and used in clinical practice.

This review provides a practical primer on AI for electrophysiologists, with the goal of supporting informed evaluation and responsible clinical adoption. We outline the historical evolution of AI, from rule-based systems to contemporary machine learning, deep learning, and emerging generative AI and large language models. Core methodological concepts are reviewed, with emphasis on data provenance, labeling, validation strategy, and the distinctions between analytical performance and clinical utility. Common failure modes are examined, including bias and lack of representativeness, overfitting, limited interpretability, workflow misalignment, and overstatement of clinical readiness.

We further discuss how regulatory agencies evaluate AI-based electrophysiology tools, what regulatory clearance establishes, and what it does not. Particular attention is given to the implications of static model review, device-specific validation, and intended use constraints, and to the continuing responsibility of clinicians in appropriate deployment and oversight.

Finally, we consider future directions for AI in electrophysiology, including individualized modeling approaches, expert decision support in resource-constrained settings, and applications aimed at improving efficiency and access to care. This review provides electrophysiologists with a practical framework to interpret current AI evidence and to actively guide how AI is evaluated, adopted, and translated to clinical practice.

•AI performance in EP is determined by model architecture, data provenance, label quality, and validation strategy.•Most EP AI failures stem from biased or narrow datasets, overfitted models, and misaligned clinical workflows.•FDA clearance confirms analytical performance under defined conditions, but not clinical effectiveness or generalizability.•Electrophysiologists must evaluate AI tools by examining intended use, data–hardware match, failure modes, and clinical risk.•Future value in EP AI lies in patient-specific modeling, and extending expert-level support to low-resource settings.

AI performance in EP is determined by model architecture, data provenance, label quality, and validation strategy.

Most EP AI failures stem from biased or narrow datasets, overfitted models, and misaligned clinical workflows.

FDA clearance confirms analytical performance under defined conditions, but not clinical effectiveness or generalizability.

Electrophysiologists must evaluate AI tools by examining intended use, data–hardware match, failure modes, and clinical risk.

Future value in EP AI lies in patient-specific modeling, and extending expert-level support to low-resource settings.