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Related Experiment Video

Updated: Jul 12, 2025

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
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Five critical quality criteria for artificial intelligence-based prediction models.

Florien S van Royen1, Folkert W Asselbergs2,3, Fernando Alfonso4

  • 1Department of General Practice & Nursing Science, Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands.

European Heart Journal
|October 28, 2023
PubMed
Summary
This summary is machine-generated.

The European Heart Journal proposes five quality criteria for clinical artificial intelligence (AI) prediction models in cardiovascular health. These standards aim to enhance the reliability and impact of AI in healthcare.

Keywords:
Artificial intelligenceDiagnosisDigital healthPredictionPrognosis

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Area of Science:

  • Cardiovascular Health
  • Artificial Intelligence
  • Clinical Prediction Modelling

Background:

  • Clinical artificial intelligence (AI) prediction modelling studies in cardiovascular health require improved quality and impact.
  • Current studies often lack standardized reporting and validation, limiting their clinical relevance.

Purpose of the Study:

  • To propose five minimal quality criteria for AI-based prediction model development and validation studies in cardiovascular health.
  • To enhance the quality, impact, and relevancy of AI in cardiovascular health research.

Main Methods:

  • The editors for digital health, innovation, and quality standards of the European Heart Journal developed the criteria.
  • Criteria focus on key aspects of AI model development and validation.

Main Results:

  • Five minimal quality criteria are proposed: complete reporting, defined intended use, rigorous validation, adequate sample size, and open code/software.
  • These criteria address critical areas for robust AI model development.

Conclusions:

  • Adherence to these five criteria is essential for improving the quality and clinical utility of AI prediction models in cardiovascular health.
  • Implementing these standards will foster greater trust and adoption of AI in cardiovascular medicine.