Kondylakis, HaridimosCiarrocchi, EstherCerda-Alberich, LeonorChouvarda, IoannaFromont, Lauren A.Garcia-Aznar, Jose ManuelKalokyri, VarvaraKosvyra, AlexandraWalker, DawnYang, GuangNeri, EmanueleThe AI4HealthImaging Working Group on metadata models2022-10-272022-10-272022Kondylakis H, Ciarrocchi E, Cerda-Alberich L, Chouvarda I, Fromont LA, Garcia-Aznar JM, Kalokyri V, Kosvyra A, Walker D, Yang G, Neri E; the AI4HealthImaging Working Group on metadata models. Position of the AI for Health Imaging (AI4HI) network on metadata models for imaging biobanks. Eur Radiol Exp. 2022 Jul 1;6(1):29. DOI: 10.1186/s41747-022-00281-12509-9280http://hdl.handle.net/10230/54622A huge amount of imaging data is becoming available worldwide and an incredible range of possible improvements can be provided by artificial intelligence algorithms in clinical care for diagnosis and decision support. In this context, it has become essential to properly manage and handle these medical images and to define which metadata have to be considered, in order for the images to provide their full potential. Metadata are additional data associated with the images, which provide a complete description of the image acquisition, curation, analysis, and of the relevant clinical variables associated with the images. Currently, several data models are available to describe one or more subcategories of metadata, but a unique, common, and standard data model capable of fully representing the heterogeneity of medical metadata has not been yet developed. This paper reports the state of the art on metadata models for medical imaging, the current limitations and further developments, and describes the strategy adopted by the Horizon 2020 "AI for Health Imaging" projects, which are all dedicated to the creation of imaging biobanks.application/pdfeng© The Author(s) under exclusive licence to European Society of Radiology. 2022 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.info:eu-repo/semantics/articlehttp://dx.doi.org/10.1186/s41747-022-00281-1Artificial intelligenceDiagnostic imagingMetadataRadiation therapyRadiomicsinfo:eu-repo/semantics/openAccess