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Evaluating artificial intelligence-enabled medical tests in cardiology: Best practice.

Jonas L Isaksen1, Malene Nørregaard1, Martin Manninger2

  • 1Laboratory of Experimental Cardiology, University of Copenhagen, Copenhagen, Denmark.

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|September 11, 2025
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Summary

This study offers best practices for evaluating machine learning in cardiovascular research, particularly cardiac electrophysiology. Key recommendations include strict data separation and comparison to non-machine learning models for robust study quality.

Keywords:
Artificial IntelligenceDeep LearningEvaluationMachine LearningMedical Test

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Area of Science:

  • Cardiovascular Research
  • Biomedical Data Science
  • Cardiac Electrophysiology

Background:

  • Machine learning (ML) is increasingly applied in cardiovascular research.
  • Cardiac electrophysiology presents unique challenges due to large, imbalanced datasets.
  • Evaluating ML studies in this field requires specific guidelines.

Purpose of the Study:

  • To highlight opportunities and challenges in evaluating ML studies in cardiovascular research.
  • To provide guidance on best practices for assessing ML applications in cardiac electrophysiology.
  • To establish a framework for the quality evaluation of ML-based medical tests.

Main Methods:

  • Utilized examples from cardiac electrophysiology research.
  • Focused on supervised machine learning study evaluation.
  • Developed recommendations for reporting and presentation of ML studies.

Main Results:

  • Recommended proper cohort selection and strict separation of training/testing data.
  • Advocated for comparison against non-ML reference models.
  • Suggested specific metrics and plots for reporting ML model performance, especially for time series and image data.

Conclusions:

  • Adherence to recommended principles ensures the quality of ML studies.
  • The proposed best practices serve as a blueprint for evaluating ML in cardiac electrophysiology and related fields.
  • Standardized evaluation methods will advance the reliable application of ML in cardiovascular medicine.