Five critical quality criteria for artificial intelligence-based prediction models

2023/10/28 by Florien S van Royen, Folkert W. Asselbergs, Fernándo Alfonso +2 · 1 voice · 72 citations
Computer Science · Medicine · #Artificial Intelligence in Healthcare and Education #Artificial intelligence #Computer science #Data mining #Machine Learning in Healthcare #Machine learning #Medicine #Pathology #Predictive modelling #Quality (philosophy) #Quality assurance #Radiomics and Machine Learning in Medical Imaging #Software

paper · pdf · doi:10.1093/eurheartj/ehad727

published in European Heart Journal 44(46), 4831-4834 (Oxford University Press)

openalex publication_date 2023/10/28 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/25

Abstract

To raise the quality of clinical artificial intelligence (AI) prediction modelling studies in the cardiovascular health domain and thereby improve their impact and relevancy, the editors for digital health, innovation, and quality standards of the European Heart Journal propose five minimal quality criteria for AI-based prediction model development and validation studies: complete reporting, carefully defined intended use of the model, rigorous validation, large enough sample size, and openness of code and software.

Citations

• Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD): Explanation and Elaboration
• Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD): The TRIPOD Statement

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Discussions

• NEW PAPER A two pager with 5 criteria to evaluate prediction models, in particular those based on AI doi.org/10.1093/eurh... [bsky, 27 points, 0 comments]

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