Artificial Intelligence for radiation protection in medical imaging and radiotherapy: A perspective from the AI Working Party of ICRP Committee 3.
Authors
Affiliations (14)
Affiliations (14)
- Department of Medical Physics, School of Medicine, University of Crete, Greece. Electronic address: [email protected].
- HUS Diagnostic Center, Radiology, University of Helsinki and Helsinki University Hospital, Helsinki, Finland.
- Department of Radiation Oncology, Stritch School of Medicine, Loyola University Chicago, USA.
- Medical Radiation Services Branch, Australian Radiation Protection and Nuclear Safety Agency, Australia.
- Department of Imaging Physics, University of Texas MD Anderson Cancer Center, USA.
- Department of Radiological Sciences, Komazawa University, Japan.
- Department of Radiology, University Putra Malaysia, Malaysia.
- Russel Morgan Department of Radiology & Radiological Science, Johns Hopkins University School of Medicine, USA.
- Hospital Network, Medical Physics Unit PT-PO, AUSL Toscana Centro, Italy.
- Department of Diagnostic Imaging, Children's Hospital Bambino Gesu IRCCSS, Italy.
- Departments of Radiation Oncology and Radiology, Feinberg School of Medicine, Northwestern University, USA.
- Office of the Deputy Director General for Operations, Federal Authority for Nuclear Regulation, United Arab Emirates.
- Department of Radiology, University of Kentucky, USA.
- Department of Medical Physics, University of Dundee, UK.
Abstract
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. 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. 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.