The integration of artificial intelligence (AI) into radiology is transforming healthcare by improving diagnostic accuracy, streamlining workflows and advancing patient care1. AI has already influenced radiography practices and could play a pivotal role in standardising patient safety2, 3. Despite these advancements, Arruzza3 and Coakley et al.4 raised concerns about role replacement and knowledge gaps among radiographers. Understanding their perspectives on AI was crucial for addressing these issues, facilitating successful integration4, 5 and enhancing human-AI collaboration to guide the evolution of radiology4, 6.
The NHS recognises AI’s potential to improve radiology practices, with the Getting It Right First Time (GIRFT)7 report and the Topol Review predicting that AI will support approximately 50% of reporting within a decade. However, current limitations underline the need to understand radiographers’ attitudes to maximise AI’s benefits.
AI is...
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