In vitro to in vivo translation of artificial intelligence for clinical use: screening for acute coronary syndrome to identify ST-elevation myocardial infarction.

Journal: Journal of the American Medical Informatics Association : JAMIA
Published Date: Jun 27, 2025

Abstract

OBJECTIVE: The integration of predictive models into live clinical care requires scientific testing before implementation to ensure patient safety. We built and technically implemented a model that predicts which patients require an electrocardiogram (ECG) to screen for heart attacks within 10 minutes of their arrival to the Emergency Department. We developed a structured framework for the in vitro to in vivo translation of the model through implementation as clinical decision support (CDS).

Authors

  • Gabrielle Bunney
    Department of Emergency Medicine, Stanford University, Palo Alto, CA 94025, United States.
  • Kate Miller
    Quantitative Sciences Unit, Stanford University, Palo Alto, CA 94025, United States.
  • Anna Graber-Naidich
    Quantitative Sciences Unit, Stanford University, Palo Alto, CA 94025, United States.
  • Rana Kabeer
    Department of Emergency Medicine, Stanford University, Palo Alto, CA 94025, United States.
  • Sean M Bloos
    Tulane University School of Medicine, New Orleans, LA 70112, United States.
  • Alexander J Wessels
    Technology and Digital Solutions, Stanford Healthcare, Palo Alto, CA 94025, United States.
  • Melissa A Pasao
    Department of Emergency Medicine, Stanford University, Palo Alto, CA 94025, United States.
  • Marium Rizvi
    Department of Emergency Medicine, Stanford University, Palo Alto, CA 94025, United States.
  • Ian P Brown
    Department of Emergency Medicine, Stanford University, Palo Alto, CA 94025, United States.
  • Maame Yaa A B Yiadom
    Department of Emergency Medicine, Stanford University, Palo Alto, CA 94025, United States.

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