Towards an accessible, centralised, searchable database for AI courses in Europe : the Artificial Intelligence in Medical Imaging and Radiation Oncology Education (AIMIROE) project

Decoster, Robin (author), Erenstein, Hendrik (author), Menzinga, Jacob (author), Cornacchione, Patrizia (author), Cunha, Altino (author), Dybeli, Elona (author), Mekiš, Nejc (author), McEntee, Mark F. (author), Precht, Helle (author), Stogiannos, Nikolaos (corresponding author)

Objective Artificial intelligence (AI) is transforming medical imaging and radiation oncology, yet limited understanding and access to education hinder adoption. This study, led by the European Society of Medical Imaging Informatics (EuSoMII) in collaboration with the European Federation of Radiographer Societies (EFRS), aimed to create an accessible, centralised, searchable database including all AI courses in Europe. Materials and methods An electronic survey was developed to collect data on European AI course characteristics, such as format, delivery, content, target audience and European Qualifications Framework (EQF) level. This was disseminated via purposive sampling through social media and mailing lists of the EuSoMII and the EFRS between September 2024 and January 2025. Quantitative data were analysed using descriptive statistics and visual representations using Python Seaborn and Geopandas. Results This study identified 29 AI courses in Europe. Of them, 53.6% were offered by universities. Courses targeted radiographers (59%), medical physicists (52%), and radiologists (41%), mainly at EQF level 7 (44.4%). Most courses were standalone (65.6%) and online (55.1%), while 41.3% were free of charge. English was the primary language of delivery (79%). Conclusions Different AI courses across Europe offer some entry-level knowledge but are often short in duration. Expanding formats, building practical competencies, providing multilingual access, and European-wide reach are essential for meaningful, practical, and equitable AI integration.

• Language: English
• Keywords: artificial intelligence, diagnostic imaging, Europe, radiation oncology, social media
• Work type: Article
• Typology: 1.01 - Original Scientific Article
• Organization: ZF - Faculty of Health Sciences
• Publication status: Published
• Publication version: Version of Record
• Publication date: 01.01.2026
• Year: 2026
• Number of pages: 10 str.
• Numbering: Vol. 10, art. 80
• PID: 20.500.12556/RUL-183010
• UDC: 616-07
• ISSN on article: 2509-9280
• DOI: 10.1186/s41747-026-00745-8
• COBISS.SI-ID: 279996163
• Publication date in RUL: 01.06.2026

Record is a part of a journal

• Title: European radiology experimental
• Shortened title: European radiol. exp.
• Publisher: Springer Nature
• ISSN: 2509-9280
• COBISS.SI-ID: 529757721

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• License: CC BY 4.0, Creative Commons Attribution 4.0 International
• Link: http://creativecommons.org/licenses/by/4.0/
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