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).
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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-pp.ucl.ac.uk/id/eprint/10180308 |
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