Five critical quality criteria for artificial intelligence-based prediction models-Reference-Cited by-同舟云学术

Five critical quality criteria for artificial intelligence-based prediction models

Published:2023-10-28 Issue:46 Volume:44 Page:4831-4834
ISSN:0195-668X
Container-title:European Heart Journal
language:en
Short-container-title:

Author:

van Royen Florien S¹^ORCID,Asselbergs Folkert W²³^ORCID,Alfonso Fernando⁴^ORCID,Vardas Panos⁵,van Smeden Maarten⁶⁷^ORCID

Affiliation:

1. Department of General Practice & Nursing Science, Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University , Utrecht , The Netherlands

2. Department of Cardiology, Amsterdam University Medical Centers, University of Amsterdam , Amsterdam , The Netherlands

3. Health Data Research UK and Institute of Health Informatics, University College London , London , UK

4. Department of Cardiology, Hospital Universitario de la Princesa, Universidad Autónoma de Madrid, IIS-IP. CIVER-CV , Madrid , Spain

5. Biomedical Research Foundation Academy of Athens (BRFAA) and Hygeia Hospitals Group , Athens , Greece

6. Department of Epidemiology & Health Economics, Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University , Universiteitsweg 100 , 3584 CG Utrecht, Netherlands

7. Department of Data Science & Biostatistics, Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University , Universiteitsweg 100, 3584 CG Utrecht , The Netherlands

Abstract

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.

Publisher

Oxford University Press (OUP)

Subject

Cardiology and Cardiovascular Medicine

Link

https://academic.oup.com/eurheartj/article-pdf/44/46/4831/54041798/ehad727.pdf

Reference33 articles.

1. Artificial intelligence and cardiology: current status and perspective;Nakamura;J Cardiol,2022

2. The year in cardiovascular medicine 2021: digital health and innovation;Vardas;Eur Heart J,2022

3. Prediction models for cardiovascular disease risk in the general population: systematic review;Damen;BMJ,2016

4. Cardiovascular risk prediction models for women in the general population: a systematic review;Baart;PLoS One,2019

5. The new European Heart Journal digital health and innovations team;Vardas;Eur Heart J,2021

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