Guiding AI in radiology: ESR’s recommendations for effective implementation of the European AI Act

This statement has been produced within the European Society of Radiology AI Working Group and identifies the key policies of the EU AI Act as they pertain to medical imaging. It offers specific recommendations to policymakers and the professional community for the effective implementation of the legislation, addressing potential gaps and uncertainties. Key areas include AI literacy, classification rules for high-risk AI systems, data governance, transparency, human oversight, quality management, deployer obligations, regulatory sandboxes, post-market monitoring, information sharing, and market surveillance. By proposing actionable solutions, the statement highlights ESR's readiness in supporting appropriate application of the AI Act in the field, promoting clarity and the effective integration of AI technologies to ensure their impactful and safe use for the benefit of Europe's patients. CRITICAL RELEVANCE STATEMENT: With the impending arrival of the EU AI Act, it is critical for stakeholders to provide timely input on its key areas. This statement offers expert feedback on the aspects of the EU AI Act that will affect medical imaging. KEY POINTS: The AI Act will significantly impact the field of medical imaging, shaping how AI technologies are used and regulated. The ESR is committed to develop guidelines and best practices, collaborating on the implementation process. This statement offers expert feedback on the aspects of the framework that will affect medical imaging.

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Publication Details

Journal
Insights into Imaging
Published
2025-02-13
DOI
https://doi.org/10.1186/s13244-025-01905-x
Citations
57
Primary Topic
Artificial Intelligence in Healthcare and Education
Type
article
Field-Weighted Citation Impact
37.30

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article

Guiding AI in radiology: ESR’s recommendations for effective implementation of the European AI Act

Merel Huisman, Arianna Bellemo, Maija Radziņa, Konstantin Nikolaou et al.
57 citations
Insights into Imaging
Artificial Intelligence in Healthcare and Education
37.30
article

Guiding AI in radiology: ESR’s recommendations for effective implementation of the European AI Act

Merel Huisman, Arianna Bellemo, Maija Radziņa, Konstantin Nikolaou, Monika Hierath, Daniel Pinto dos Santos, Susan Cheng Shelmerdine, Tugba Akinci D’Antonoli, Renato Cuocolo, Luis Martí‐Bonmatí, Michail E. Klontzas, Matthias May, Elmar Kotter, Emanuele Neri
article en
57 citations

Abstract

This statement has been produced within the European Society of Radiology AI Working Group and identifies the key policies of the EU AI Act as they pertain to medical imaging. It offers specific recommendations to policymakers and the professional community for the effective implementation of the legislation, addressing potential gaps and uncertainties. Key areas include AI literacy, classification rules for high-risk AI systems, data governance, transparency, human oversight, quality management, deployer obligations, regulatory sandboxes, post-market monitoring, information sharing, and market surveillance. By proposing actionable solutions, the statement highlights ESR's readiness in supporting appropriate application of the AI Act in the field, promoting clarity and the effective integration of AI technologies to ensure their impactful and safe use for the benefit of Europe's patients. CRITICAL RELEVANCE STATEMENT: With the impending arrival of the EU AI Act, it is critical for stakeholders to provide timely input on its key areas. This statement offers expert feedback on the aspects of the EU AI Act that will affect medical imaging. KEY POINTS: The AI Act will significantly impact the field of medical imaging, shaping how AI technologies are used and regulated. The ESR is committed to develop guidelines and best practices, collaborating on the implementation process. This statement offers expert feedback on the aspects of the framework that will affect medical imaging.

Insights into ImagingVol. 16(1)
University of Pisa (IT), Goethe University Frankfurt (DE), University of Salerno (IT), University of Crete (GR), Radboud University Nijmegen (NL), University of Freiburg (DE), Friedrich-Alexander-Universität Erlangen-Nürnberg (DE), Great Ormond Street Hospital (GB), University Medical Center Freiburg (DE), European Society of Radiology (AT), Radboud University Medical Center (NL), Hospital Universitari i Politècnic La Fe (ES), Universitätsklinikum Erlangen (DE), Riga Stradiņš University (LV), University Hospital Frankfurt (DE), University Hospital Cologne (DE), NIHR Great Ormond Street Hospital Biomedical Research Centre (GB), University College London (GB), University of Tübingen (DE), University of Latvia (LV)
National Institute for Health and Care Research, Friedrich-Alexander-Universität Erlangen-Nürnberg, Radboud Universiteit, Albert-Ludwigs-Universität Freiburg, Great Ormond Street Hospital for Children, University of Crete, Latvijas Universitate, Radboud Universitair Medisch Centrum, Università degli Studi di Salerno, Università di Pisa, Universitätsklinikum Köln, Great Ormond Street Institute of Child Health, Medizinische Fakultät der Albert-Ludwigs-Universität Freiburg
Quality Education
Openalex Percentile: Top 0%
Artificial Intelligence in Healthcare and Education
37.30
DOIOpen Access PDFOpenAlex
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