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<b>Objective</b> <p>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.</p> <b>Materials and methods</b> <p>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.</p> <b>Results</b> <p>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%).</p> <b>Conclusions</b> <p>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.</p> <b>Relevance statement</b> <p>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.</p> Key Points <p><UnorderedList Mark="Bullet"> <ItemContent> <p>We identified 29 different AI European courses varying in language, content, and delivery.</p> </ItemContent> <ItemContent> <p>Many clinical practitioners and researchers are unaware of these resources.</p> </ItemContent> <ItemContent> <p>We need a centralised database for customising AI learning choices and guiding future course design.</p> </ItemContent> </UnorderedList></p> Graphical Abstract <p></p>

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

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年5月29日

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  • Robin Decoster,
  • Hendrik Erenstein,
  • Jacob Menzinga,
  • Patrizia Cornacchione,
  • Altino Cunha,
  • Elona Dybeli,
  • Nejc Mekis,
  • Mark McEntee,
  • Karoliina Paalimäki-Paakki,
  • Helle Precht,
  • Tugba Akinci D’Antonoli,
  • Renato Cuocolo,
  • Merel Huisman,
  • Michail E. Klontzas,
  • Elmar Kotter,
  • Daniel Pinto dos Santos,
  • Erik Ranschaert,
  • Peter van Ooijen,
  • Nikolaos Stogiannos,
  • Christina Malamateniou

摘要

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.

Graphical Abstract