What does this research mean for the field?

Current AI education in European medical imaging and radiation oncology consists of fragmented, predominantly short, online, and English-language courses, necessitating a centralized database to improve accessibility and guide future training. Novelty: ClaimNovelty.INCREMENTAL. Consensus alignment: ConsensusAlignment.NEUTRAL.

What question did this study set out to answer?

The aim is to establish a centralized database to enhance access to AI education in medical imaging and radiation oncology across Europe.

May 31, 2026Open Access

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

Key Points

The aim is to establish a centralized database to enhance access to AI education in medical imaging and radiation oncology across Europe.
An electronic survey was conducted to gather data on AI course characteristics in Europe.
Data collection utilized purposive sampling via social media and mailing lists between September 2024 and January 2025.
Quantitative analysis was performed using descriptive statistics and visualization techniques in Python.
Identified 29 AI courses in Europe, with 53.6% offered by universities.
Courses predominantly targeted radiographers (59%), medical physicists (52%), and radiologists (41%), mainly at EQF level 7 (44.4%).
65.6% of courses were standalone, 55.1% online, and 41.3% were free, with English as the primary language of delivery (79%).

Abstract

Abstract 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. Relevance statement With the scaling-up of AI adoption in medical imaging and radiation oncology, there is a variety of AI education provisions currently available. Accessing these options via an open, centralised, regularly updated database enables people to make an informed decision about their training and practise safely and meaningfully. Key Points We identified 29 different AI European courses varying in language, content, and delivery. Many clinical practitioners and researchers are unaware of these resources. We need a centralised database for customising AI learning choices and guiding future course design.

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