AI has great potential to improve medical radiation protection but its safe use requires careful management of associated risks, and this perspective provides a framework to support future ICRP recommendations on the safe integration of AI into medical radiation protection.
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.
Radiation oncology is a highly digital and data-intensive discipline in which artificial intelligence (AI) is increasingly being incorporated into the clinical workflow. From image analysis and automatic segmentation to treatment planning, adaptive radiotherapy, radiomics, and outcome prediction, AI has the potential t...
S. Ichou, K. Nouni, A. Lachgar et al.· MedPeer publisher· 0 citations
The findings highlight the need for evidence from large, multicenter, prospective trials and evaluation frameworks that reflect the consequences of clinical decision-making, as well as further exploration of safeguards to monitor and address mismatches between training data and incoming scans during deployment.
Lisa Koopmans, Fernando Vega Lara, Christian Roest et al.· Abdominal Radiology· 0 citations
Artificial intelligence (AI) is rapidly changing many medical specialties, and Radiation Therapy is one of them. This paper deals with the adoption of AI in Radiation Therapy, its benefits and its challenges. Artificial intelligence can improve the accuracy, speed, and results of radiation therapy and may also aid in a...
Rahul Rai, Krishna Kumar Singh· International Journal of Inn...· 0 citations
Overall, AI is becoming an integral component of modern oncology, particularly radiation oncology, and its successful integration into routine clinical practice will require robust validation, transparent governance, equitable implementation, and continued clinician oversight to ensure safe, effective, and patient-cent...
K. Rastogi· The Rise of Artificial Intel...· 0 citations
This narrative review examines the evolution of artificial intelligence (AI) in healthcare, with a focus on the transition from early rule-based systems to modern deep learning architectures and their integration into clinical practice. We examine foundational technologies, including convolutional neural networks for i...
Abdulkadir Yıldırım, Ö. Özdemi̇r· Artificial Intelligence in M...· 0 citations
Artificial intelligence (AI) and radiomics have emerged as promising approaches in lung-cancer imaging by extracting quantitative features from routine medical images beyond visual assessment alone. Proof-of-concept studies span pulmonary nodule characterisation, molecular biomarker prediction, treatment-response asses...
J. Naidu, V. Baskaradoss· Academia Medical Imaging and...· 0 citations
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.