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Five critical quality criteria for artificial intelligence-based prediction models

van Royen, Florien S; Asselbergs, Folkert W; Alfonso, Fernando; Vardas, Panos; van Smeden, Maarten; (2023) Five critical quality criteria for artificial intelligence-based prediction models. European Heart Journal 10.1093/eurheartj/ehad727. (In press). Green open access

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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.

Type: Article
Title: Five critical quality criteria for artificial intelligence-based prediction models
Location: England
Open access status: An open access version is available from UCL Discovery
DOI: 10.1093/eurheartj/ehad727
Publisher version: https://doi.org/10.1093/eurheartj/ehad727
Language: English
Additional information: This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial License (https://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com.
Keywords: Artificial intelligence, Diagnosis, Digital health, Prediction, Prognosis
UCL classification: UCL
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > Institute of Health Informatics
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > Institute of Health Informatics > Clinical Epidemiology
URI: https://discovery.ucl.ac.uk/id/eprint/10180308
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