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

TY - JOUR

T1 - Artificial Intelligence for radiation protection in medical imaging and radiotherapy

T2 - A perspective from the AI Working Party of ICRP Committee 3

AU - Damilakis, John

AU - Kortesniemi, Mika

AU - Gros, Sebastien

AU - Williams, Ivan

AU - Ocampo, Juan

AU - Inoue, Yusuke

AU - Noor, Noramaliza

AU - Mahesh, Mahadevappa

AU - Mazzoni, Lorenzo Nicola

AU - Magistrelli, Andrea

AU - Woloschak, Gayle

AU - Giuffrida, Daniele

AU - Applegate, Kimberly

AU - Sutton, David

N1 - Copyright © 2026 Associazione Italiana di Fisica Medica e Sanitaria. Published by Elsevier Ltd. All rights reserved.

PY - 2026/8

Y1 - 2026/8

N2 - 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.

AB - 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.

KW - Artificial Intelligence

KW - Radiation Protection/methods

KW - Radiotherapy

KW - Humans

KW - Diagnostic Imaging

KW - Medical imaging

UR - https://www.scopus.com/pages/publications/105045368129

U2 - 10.1016/j.ejmp.2026.105906

DO - 10.1016/j.ejmp.2026.105906

M3 - Review article

C2 - 42501579

SN - 1120-1797

VL - 148

SP - 105906

JO - Physica Medica

JF - Physica Medica

M1 - 105906

ER -