
Radiographers and radiological technologists shouldn't be left out of the process of developing and implementing artificial intelligence (AI) applications, according to a joint statement from the International Society of Radiographers and Radiological Technologists and the European Federation of Radiographer Societies.
"It is of critical importance that radiographers and radiological technologists, as medical imaging and radiotherapy experts, must play an active role in the planning, development, implementation, use and validation of AI applications in medical imaging and radiation therapy, reinforcing the need for the technology to be targeted to the most pressing clinical problems," the societies wrote in a guest editorial published online on 3 April in Radiography.
As adoption of AI in medical imaging and radiation therapy requires radiographers and radiological technologists to adapt their imaging and treatment practices, these changes in practice must be underpinned by appropriate education and training -- both for the existing workforce and in curriculum development for the future workforce, the societies said.
The editorial also summarizes the societies' positions on how AI should be adopted in radiography and the roles of radiographers and radiological technologists in optimizing the use of AI.
"The optimal integration of AI into medical safety, clinical imaging and radiation therapy can only be achieved through appropriate education of the current and future workforce and the active engagement of radiographers and radiologic technologists in AI advancements going forward," they wrote.



![A normal mammogram confirmed by three-year radiologic follow-up illustrates reader-marked regions of interest (ROIs) during (A) unaided (round 1) and (B) artificial intelligence (AI)–assisted (round 2) reading. Each colored dot represents an ROI for recall by a human reader. Readers could mark more than one ROI per case, represented by multiple dots of the same color. During AI-assisted reading, the AI system displayed three visible prompts: two with suspicion of malignancy scores of 35% (left mediolateral oblique [L MLO] and craniocaudal [L CC]) and one with a suspicion of malignancy score of 10% (right craniocaudal [R CC]), shown as polygonal overlays. Without AI, six of 10 readers (60%) marked a false-positive ROI. With AI assistance, this fell to two of 10 (20%). R MLO = right mediolateral oblique.](https://img.auntminnieeurope.com/mindful/smg/workspaces/default/uploads/2026/07/2026-07-14-radiology-mammogram-ai-auto-bias.H0bYO8QlWs.jpg?auto=format%2Ccompress&fit=crop&h=100&q=70&w=100)







![A normal mammogram confirmed by three-year radiologic follow-up illustrates reader-marked regions of interest (ROIs) during (A) unaided (round 1) and (B) artificial intelligence (AI)–assisted (round 2) reading. Each colored dot represents an ROI for recall by a human reader. Readers could mark more than one ROI per case, represented by multiple dots of the same color. During AI-assisted reading, the AI system displayed three visible prompts: two with suspicion of malignancy scores of 35% (left mediolateral oblique [L MLO] and craniocaudal [L CC]) and one with a suspicion of malignancy score of 10% (right craniocaudal [R CC]), shown as polygonal overlays. Without AI, six of 10 readers (60%) marked a false-positive ROI. With AI assistance, this fell to two of 10 (20%). R MLO = right mediolateral oblique.](https://img.auntminnieeurope.com/mindful/smg/workspaces/default/uploads/2026/07/2026-07-14-radiology-mammogram-ai-auto-bias.H0bYO8QlWs.jpg?auto=format%2Ccompress&fit=crop&h=112&q=70&w=112)







