Radiographers’ Perceptions on Using Artificial Intelligence in Computed Tomography and on the Knowledge and Professional Skills It Requires from a Radiographer : a qualitative study

Abstract

The role of artificial intelligence (AI) has increased in medical imaging, creating challenges for the required knowledge and professional skills of radiographers in the future. Recent studies have shown the need to integrate AI training into radiographer education. The purpose of this master’s thesis was to understand radiographers as end users of AI in computed tomography (CT), before formal AI training is deployed in radiographer education in Finland. The aim of this master’s thesis was to find out radiographers’ perceptions on using AI in CT and on the knowledge and professional skills it requires from a radiographer. The data collection was carried out as an online survey questionnaire with open-ended questions, with the participation of 23 radiographers from HUS Diagnostic Center, Finland. The data was analysed using the method of inductive content analysis. The six main categories answered the research questions about how radiographers conceive AI is used in CT, what kind of challenges and opportunities AI in CT might bring to their profession, and what kind of knowledge and professional skills they consider important in this context. Some of the radiographers thought AI was used in CT throughout the imaging process. For some, AI’s existence in CT was uncertain. Both, the perceived challenges and opportunities were linked to AI-enabled automation. Radiographers were perceived to have oversight of AI. The prerequisites of radiographer oversight over AI were to have a strong knowledge base and basic professional skills in CT, combined with a new knowledgebase on AI. The required knowledge base on AI was described to consist of knowledge acquired from radiographer education, and from orientation and training provided by employers and CT manufacturers. Radiographers should be educated to use AI in their work. Radiographers’ AI-related education and training should be planned to enable the radiographer’s overseeing role regarding AI. The results may be used by educational institutions when planning radiographer education focusing on AI, radiology departments when planning their internal training and orientations, and CT manufacturers when planning the end user training of their products.