Artificial Intelligence for radiation protection in medical imaging and radiotherapy: A perspective from the AI Working Party of ICRP Committee 3

PURPOSE: The increasing integration of Artificial Intelligence (AI) into clinical workflows for medical imaging and radiotherapy presents new opportunities and challenges for the radiation protection of patients, staff, and the public. This perspective from the International Commission on Radiological Protection (ICRP) Committee 3 Working Party on AI examines how current and emerging AI applications support the core principles of justification and optimisation across diagnostic and interventional radiology, nuclear medicine, and radiotherapy, and identifies priorities for their safe clinical implementation. MAIN FINDINGS: AI applications with the greatest current clinical maturity include clinical decision support for referral appropriateness, image reconstruction, protocol optimisation, automated contouring, treatment planning, adaptive radiotherapy, and AI-enabled quality assurance. Other applications, including patient-specific dosimetry, occupational dose prediction, synthetic imaging, and predictive safety analytics, show considerable promise but remain at earlier stages of validation. Significant challenges accompany these advances: data biases and limited generalisability may undermine performance across diverse settings; the "black box" nature of many models complicates clinical accountability; and robust validation, continuous quality assurance, and harmonised regulatory oversight remain essential. Dedicated training in AI literacy for healthcare professionals is critical for safe deployment. CONCLUSION: AI has great potential to improve medical radiation protection but its safe use requires careful management of associated risks. By identifying areas of established clinical adoption, emerging applications, and common implementation priorities, this perspective provides a framework to support future ICRP recommendations on the safe integration of AI into medical radiation protection.

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Journal
Physica Medica
Published
2026-07-25
DOI
https://doi.org/10.1016/j.ejmp.2026.105906
Primary Topic
Advanced Radiotherapy Techniques
Type
article
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article

Artificial Intelligence for radiation protection in medical imaging and radiotherapy: A perspective from the AI Working Party of ICRP Committee 3

L.N. Mazzoni, Ivan Williams, Yusuke Inoue, Mahadevappa Mahesh et al.
Physica Medica
Advanced Radiotherapy Techniques
article

Artificial Intelligence for radiation protection in medical imaging and radiotherapy: A perspective from the AI Working Party of ICRP Committee 3

L.N. Mazzoni, Ivan Williams, Yusuke Inoue, Mahadevappa Mahesh, Sébastien Gros, Andrea Magistrelli, Mika Kortesniemi, John Damilakis, Gayle Woloschak, David Sutton, Juan Camilo Ocampo Ramos, Kimberly Applegate, Daniele Giuffrida, Noramaliza Noor
article en

Abstract

PURPOSE: The increasing integration of Artificial Intelligence (AI) into clinical workflows for medical imaging and radiotherapy presents new opportunities and challenges for the radiation protection of patients, staff, and the public. This perspective from the International Commission on Radiological Protection (ICRP) Committee 3 Working Party on AI examines how current and emerging AI applications support the core principles of justification and optimisation across diagnostic and interventional radiology, nuclear medicine, and radiotherapy, and identifies priorities for their safe clinical implementation. MAIN FINDINGS: AI applications with the greatest current clinical maturity include clinical decision support for referral appropriateness, image reconstruction, protocol optimisation, automated contouring, treatment planning, adaptive radiotherapy, and AI-enabled quality assurance. Other applications, including patient-specific dosimetry, occupational dose prediction, synthetic imaging, and predictive safety analytics, show considerable promise but remain at earlier stages of validation. Significant challenges accompany these advances: data biases and limited generalisability may undermine performance across diverse settings; the "black box" nature of many models complicates clinical accountability; and robust validation, continuous quality assurance, and harmonised regulatory oversight remain essential. Dedicated training in AI literacy for healthcare professionals is critical for safe deployment. CONCLUSION: AI has great potential to improve medical radiation protection but its safe use requires careful management of associated risks. By identifying areas of established clinical adoption, emerging applications, and common implementation priorities, this perspective provides a framework to support future ICRP recommendations on the safe integration of AI into medical radiation protection.

Physica MedicaVol. 148
Northwestern University (US), Dubai Health Authority (AE), Universiti Putra Malaysia (MY), University of Helsinki (FI), The University of Texas MD Anderson Cancer Center (US), University of Crete (GR), University of Kentucky (US), Johns Hopkins University (US), University of Dundee (GB), Loyola University Chicago (US), Johns Hopkins Medicine (US), Helsinki University Hospital (FI), Bambino Gesù Children's Hospital (IT), Nuclear and Radiation Safety Center (CN), Department of Statistics Malaysia (MY), Azienda Usl Toscana Centro (IT), Komazawa University (JP)
Openalex Percentile: Top 9%
Advanced Radiotherapy Techniques
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