Health AI Central
Peer-Reviewed Publication
JMIR AI2026;5e81472.October 15, 2026Journal Article

Quality Assessment and Model Training Impact of Synthetic Images in Medical Diagnostic Imaging: Systematic Review.

Júlia Folguera-Profitós1,2, Marina Ramiro-Pareta2,3, Gerard Carot-Sans1,2, Alba Jiménez-Rueda2,3, Júlia Aznar-Roig4, Didier Domínguez-Herrera3, Maria Mercedes Nogueras2,3, Esmaeil Nadimi5, Ferran Torres6, Jordi Piera-Jiménez2,3
1Information Systems Department, Catalan Health Service (CatSalut), Gran Via de les Corts Catalanes, 587, Barcelona, 08007, Spain, +34 93 403 85 85.
2Digitalization for the Sustainability of the Heathcare System (DS3) Research Group, Barcelona, Spain.
3Fundació Tic Salut i Social (FTSS), Barcelona, Spain.
4Process Unit, Directorate of Quality, Processes and Innovation, Vall d'Hebron University Hospital, Barcelona, Spain.
5Faculty of Engineering, Maersk Mc-Kinney Moller Institute, University of Southern Denmark, Odense, Denmark.
6Biostatistics Unit, Medical School, Universitat Autònoma de Barcelona, Bellaterra, Spain.
PubMed DOI

Abstract

BACKGROUND: AI has increasingly been used to generate synthetic images to overcome the scarcity of large, regulatory-compliant medical image datasets. However, their quality and actual utility for algorithm development across deep learning architectures, medical settings, and image types have been poorly assessed. OBJECTIVE: This study aimed to review the use of synthetic images for algorithm dev…

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