A Nordic survey on artificial intelligence in the radiography profession - Is the profession ready for a culture change?

INTRODUCTION: The impact of artificial intelligence (AI) on the radiography profession remains uncertain. Although AI has been increasingly used in clinical radiography, the perspectives of the radiography professionals in Nordic countries have yet to be examined. The primary aim was to examine views of Nordic radiographers 'on AI, with focus on perspectives, engagement, and knowledge of AI. METHODS: Radiographers from Denmark, Norway, Sweden, Iceland, Greenland, and the Faroe Island were invited through social media platforms to participate in an online survey from March to June 2023. The survey encompassed 29-items and included 4 sections a) demographics, b) barriers and enablers on AI, c) perspectives and experiences of AI and d) knowledge of AI in radiography. Edgars Schein's model of organizational culture was employed to analyse Nordic radiographers' perspectives on AI. RESULTS: Overall, a total of 421 respondents participated in the survey. A majority were positive/somewhat positive towards AI in radiography e.g., 77.9 % (n = 342) thought that AI would have a positive effect on the profession, and 26% thought that AI would reduce the administrative workload. Most radiographers agreed or strongly agreed that clinicians may have access to AI generated reports (76.8 %, n = 297). Nevertheless, a total of 86 (20.1%) agree or somewhat agreed that AI a potential risk for radiography. CONCLUSION: Nordic radiographers are generally positive towards AI, yet uncertainties regarding its implementation persist. The findings underscore the importance of understanding these challenges for the responsible integration of AI systems. Carefully weighing the expected influence of AI against key incentives will support a seamless integration of AI for the benefit not just of the patients, but also of the radiography profession. IMPLICATIONS FOR PRACTICE: Understanding incentives factors and barriers can help address uncertainties during implementation of AI in clinical practice.

Nordic radiographers' and students' perspectives on artificial intelligence - A cross-sectional online survey.

INTRODUCTION: The integration of artificial intelligence (AI) into the domain of radiography holds substantial potential in various aspects including workflow efficiency, image processing, patient positioning, and quality assurance. The successful implementation of AI within a Radiology department necessitates the participation of key stakeholders, particularly radiographers. The study aimed to provide a comprehensive investigation about Nordic radiographers' perspectives and attitudes towards AI in radiography. METHODS: An online 29-item survey was distributed via social media platforms to Nordic students and radiographers working in Denmark, Norway, Sweden, Iceland, Greenland, and the Faroe Islands including items on demographics, specialization, educational background, place of work and perspectives and knowledge on AI. The items were a mix of closed-type and scaled questions, with the option for free-text responses when relevant. RESULTS: The survey received responses from all Nordic countries with 586 respondents, 26.8% males, 72.1% females, and 1.1% non-binary/self-defined or preferred not to say. The mean age was 37.2 with a standard deviation (SD) of ±12.1 years, and the mean number of years since qualification was 14.2 SD ± 10.3 years. A total of 43% (n = 254) of the respondents had not received any AI training in clinical practice. Whereas 13% (n = 76) had received AI during radiography undergrad training. A total of 77.9% (n = 412) expressed interest in pursuing AI education. The majority of respondents were aware of the potential use of AI (n = 485, 82.8%) and 39.1% (n = 204) had no reservations about AI. CONCLUSION: Overall, this study found that Nordic radiographers have a positive attitude toward AI. Very limited training or education has been provided to the radiographers. Especially since 82.8% reports on plans to implement AI in clinical practice. In general, awareness of AI applications is high, but the educational level is low for Nordic radiographers. IMPLICATION FOR PRACTICE: This study emphasises the favourable view of AI held by students and Nordic radiographers. However, there is a need for continuous professional development to facilitate the implementation and effective utilization of AI tools within the field of radiography.

Workplace factors facilitating the radiographers' assessment of referrals for diagnostic imaging - A qualitative study.

INTRODUCTION: Radiology referrals are assessed for appropriate imaging based on the available clinical information. The task is legally the responsibility of the radiologists but could be delegated to radiographers under guidance. Knowledge of how this task is organised in radiology departments is limited. The study aim was to identify workplace factors facilitating the radiographers' assessment of referrals for medical imaging. METHODS: Five radiographers were recruited by convenience- and snowball-sampling techniques through the online social media platform LinkedIn. The participants represented different private and public hospitals and had from three to above ten years of experience with assessment of referrals for plain and cross sectional imaging. Following a qualitative approach, 60-min in-depth semi-structured interviews were conducted through online video meetings. Interviews followed a topic guide with 15 questions and 20 keywords, previously tested through a pilot interview. Systematic text condensation was performed using NVivo 12, where central themes and underlying subthemes were developed. RESULTS: Five central facilitating factors were identified, each with subthemes identified as: (1) Formal responsibilities; Documented delegation, Specific role description, (2) Training; Achieving skills, Maintaining skills, (3) Guidelines; Clinical indications, Priority, (4) Resource allocation; Time, Staff, (5) a Supporting environment; Teamwork, Mutual benefits, Feedback and knowledge sharing. CONCLUSION: The study adds new and valuable insights into workplace factors facilitating the radiographers' delegated task of assessing referrals. Workflows adapting such factors benefit radiographers by increasing knowledge and professional development, while positively re-allocating radiologist resources. IMPLICATIONS FOR PRACTICE: The study findings may support radiology workplaces in establishing or improving referral assessment by radiographers. Subsequently, improved quality of patient services may be achieved.