Beauty Is in the AI of the Beholder: Are We Ready for the Clinical Integration of Artificial Intelligence in Radiography? An Exploratory Analysis of Perceived AI Knowledge, Skills, Confidence, and Education Perspectives of UK Radiographers - Rainey, Clare; O'Regan, Tracy; Matthew, Jacqueline; Skelton, Emily; Woznitza, Nick; Chu, Kwun-Ye; Goodman, Spencer; McConnell, Jonathan; Hughes, Ciara; Bond, Raymond; McFadden, Sonyia; Malamateniou, Christina (Frontiers in Digital Health, 2021)
Clinical integration of AI is already well underway. Previous work was done on the AI knowledge and perceptions of radiologists/medical staff and students however there is lack of information regarding radiographers. Published literature agrees that AI will have significant impact on radiology practice. An awareness of the current level of radiographers’ perceived knowledge, skills and confidence in AI is essential to identify any educational needs necessary for successful adoption into practice as radiographers work on the front-line of service delivery. The aim was to determine the perceived knowledge, skills and confidence in AI amongst UK radiographers and highlight priorities for educational provisions to support a digital healthcare ecosystem. A survey was created on Qualtrics®, promoted via social media, open to all UK radiographers. and used snowball sampling. Demographic information and data on the perceived, self-reported, knowledge, skills and confidence in AI of radiographers was collected. Quantitative analysis used SPSS® and qualitative thematic analysis was performed on NVivo®. Results 411 responses were collected (80% from diagnostic radiography and 20% from radiotherapy). There was a notable lack of sufficient knowledge of AI principles, understanding of AI terminology, skills and confidence in the use of AI technology. Many participants, (57% of diagnostic/49% radiotherapy) respondents, do not feel adequately trained to implement AI in the clinical setting. Furthermore 52% and 64% respectively said they have not developed any skill in AI whilst 62% and 55% respectively felt there was not enough AI training for radiographers. The majority of the respondents indicate that there is an urgent need for further education and training in artificial intelligence), with many respondents stating that they had to educate themselves to gain some basic AI skills. Notable correlations between confidence in working with AI and with gender, age and highest qualification were reported. Conclusion The results of this survey highlight the perceived lack of knowledge, skills and confidence for radiographers in applying AI solutions but also underline the need for formalised education on AI to safely and efficiently navigate a digital future. Focus should be given on different needs of learners depending on age, gender and highest qualification.