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AI in the EP lab - mapping, imaging, and signal interpretation.

Abhinav B Anand1, Ketan Rajawat2

  • 1Department of Cardiology, Narayana Health, Mysuru, India.

Indian Pacing and Electrophysiology Journal
|May 27, 2026
PubMed
Summary

Artificial intelligence (AI) enhances arrhythmia diagnosis and treatment by analyzing patient data for personalized care. AI in electrophysiology (EP) labs streamlines workflows, improves substrate identification, and aids procedural planning with cardiac imaging and digital twins.

Keywords:
Artificial intelligenceAtrial fibrillationElectrophysiologyMachine learningVentricular tachycardia

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Area of Science:

  • Cardiology
  • Medical Imaging
  • Artificial Intelligence

Background:

  • Artificial intelligence (AI) leverages computational models to interpret complex cardiac data, including electrical signals and imaging.
  • AI applications in electrophysiology (EP) aim to enhance arrhythmia diagnosis, treatment, and procedural decision-making.
  • Personalized computational models create cardiac digital twins for procedural planning and hypothesis testing.

Purpose of the Study:

  • To review current evidence on AI applications in the electrophysiology (EP) lab.
  • To highlight AI's role in preprocedural planning, intraprocedural imaging, and the use of cardiac digital twins.
  • To discuss practical workflows, clinical use cases, and limitations of AI in EP.

Main Methods:

  • Synthesis of current evidence on AI in the EP lab.
  • Review of AI integration with cardiac CT and MRI for automated segmentation and characterization.
  • Analysis of AI-guided approaches for atrial fibrillation and ventricular tachycardia.

Main Results:

  • AI integration with cardiac imaging automates segmentation and scar characterization, aiding in identifying ventricular tachycardia substrates.
  • AI-guided approaches for atrial fibrillation show improved procedural consistency and clinical outcomes.
  • Cardiac digital twins enable personalized procedural planning and hypothesis testing.

Conclusions:

  • AI significantly enhances arrhythmia diagnosis and treatment by providing personalized insights and automating complex tasks.
  • AI integration in the EP lab promises to reduce variability, improve substrate identification, and streamline workflows.
  • Further development and validation are needed to overcome key limitations and fully realize AI's potential in cardiac electrophysiology.