So You’ve Got a High AUC, Now What? An Overview of Important Considerations when Bringing Machine-Learning Models from Computer to Bedside

Jiawen Deng, Mohamed E. Elghobashy, Kathleen Zang, Shubh K. Patel, Eddie Guo and Kiyan Heybati
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Jiawen Deng: Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada
Mohamed E. Elghobashy: Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada
Kathleen Zang: Dalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada
Shubh K. Patel: Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada
Eddie Guo: Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
Kiyan Heybati: Mayo Clinic Alix School of Medicine, Mayo Clinic, Rochester, MN, USA

Medical Decision Making, 2025, vol. 45, issue 6, 640-653

Abstract: Machine-learning (ML) models have the potential to transform health care by enabling more personalized and data-driven clinical decision making. However, their successful implementation in clinical practice requires careful consideration of factors beyond predictive accuracy. We provide an overview of essential considerations for developing clinically applicable ML models, including methods for assessing and improving calibration, selecting appropriate decision thresholds, enhancing model explainability, identifying and mitigating bias, as well as methods for robust validation. We also discuss strategies for improving accessibility to ML models and performing real-world testing. Highlights This tutorial provides clinicians with a comprehensive guide to implementing machine-learning classification models in clinical practice. Key areas covered include model calibration, threshold selection, explainability, bias mitigation, validation, and real-world testing, all of which are essential for the clinical deployment of machine-learning models. Following these guidance can help clinicians bridge the gap between machine-learning model development and real-world application and enhance patient care outcomes.

Keywords: machine learning; external validation; model calibration; bedside deployment; health informatics (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:sae:medema:v:45:y:2025:i:6:p:640-653

DOI: 10.1177/0272989X251343082

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