Radiographers' knowledge, attitudes and expectations of artificial intelligence in medical imaging. Issue 4 (November 2022)

• Record Type: Journal Article
• Authors: Coakley, S.; Young, R.; Moore, N.; England, A.; O'Mahony, A.; O'Connor, O.J.; Maher, M.; McEntee, M.F.
• Is Part Of: Radiography. Volume 28:Issue 4(2022)
• Page Start: 943
• Page End: 948
• Publication Date: 2022-11
• Subjects: Artificial intelligence -- Radiography -- Medical imaging -- Europe -- Survey -- Education; Diagnostic imaging -- Periodicals; Radiotherapy -- Periodicals; Cancer -- Radiotherapy -- Periodicals; Diagnostic Imaging -- Periodicals; Neoplasms -- Periodicals; Radiotherapy -- Periodicals; Imagerie pour le diagnostic -- Périodiques; Radiothérapie -- Périodiques; Cancer -- Radiothérapie -- Périodiques; Electronic journals; 616.0757
• DOI: 10.1016/j.radi.2022.06.020
• Languages: English
• ISSNs: 1078-8174

Abstract

Abstract: Introduction: Artificial intelligence (AI) is increasingly utilised in medical imaging systems and processes, and radiographers must embrace this advancement. This study aimed to investigate perceptions, knowledge, and expectations towards integrating AI into medical imaging amongst a sample of radiographers and determine the current state of AI education within the community. Methods: A cross-sectional online quantitative study targeting radiographers based in Europe was conducted over ten weeks. Captured data included demographical information, participants' perceptions and understanding of AI, expectations of AI and AI-related educational backgrounds. Both descriptive and inferential statistical techniques were used to analyse the obtained data. Results: A total of 96 valid responses were collected. Of these, 64% correctly identified the true definition of AI from a range of options, but fewer (37%) fully understood the difference between AI, machine learning and deep learning. The majority of participants (83%) agreed they were excited about the advancement of AI, though a level of apprehensiveness remained amongst 29%. A severe lack of education on AI was noted, with only 8% of participants having received AI teachings in their pre-registration qualification. Conclusion: Overall positive attitudes towards AI implementation were observed. The slight apprehension may stem from the lack of technical understanding of AI technologies and AI training within the community. Greater educational programs focusing on AI principles are required to help increase European radiography workforce engagement and involvement in AI technologies. Implications for practice: This study offers insight into the current perspectives of European based radiographers on AI in radiography to help facilitate the embracement of AI technology and convey the need for AI-focused education within the profession.