Professionals’ responses to the introduction of AI innovations in radiology and their implications for future adoption: a qualitative study

Abstract

Background Artificial Intelligence (AI) innovations in radiology offer a potential solution to the increasing demand for imaging tests and the ongoing workforce crisis. Crucial to their adoption is the involvement of different professional groups, namely radiologists and radiographers, who work interdependently but whose perceptions and responses towards AI may differ. We aim to explore the knowledge, awareness and attitudes towards AI amongst professional groups in radiology, and to analyse the implications for the future adoption of these technologies into practice. Methods We conducted 18 semi-structured interviews with 12 radiologists and 6 radiographers from four breast units in National Health Services (NHS) organisations and one focus group with 8 radiographers from a fifth NHS breast unit, between 2018 and 2020. Results We found that radiographers and radiologists vary with respect to their awareness and knowledge around AI. Through their professional networks, conference attendance, and contacts with industry developers, radiologists receive more information and acquire more knowledge of the potential applications of AI. Radiographers instead rely more on localized personal networks for information. Our results also show that although both groups believe AI innovations offer a potential solution to workforce shortages, they differ significantly regarding the impact they believe it will have on their professional roles. Radiologists believe AI has the potential to take on more repetitive tasks and allow them to focus on more interesting and challenging work. They are less concerned that AI technology might constrain their professional role and autonomy. Radiographers showed greater concern about the potential impact that AI technology could have on their roles and skills development. They were less confident of their ability to respond positively to the potential risks and opportunities posed by AI technology. Conclusions In summary, our findings suggest that professional responses to AI are linked to existing work roles, but are also mediated by differences in knowledge and attitudes attributable to inter-professional differences in status and identity. These findings question broad-brush assertions about the future deskilling impact of AI which neglect the need for AI innovations in healthcare to be integrated into existing work processes subject to high levels of professional autonomy.

Acceptance of clinical artificial intelligence among physicians and medical students: A systematic review with cross-sectional survey

“…The support rate for the statement that AI could replace human physicians ranged from 6 to 78% ( 19 , 37 , 58 ), of which 31 (77.50%) studies showed that the support rate was less than half ( 11 , 16 – 19 , 21 , 22 , 24 , 25 , 30 , 33 – 37 , 39 – 42 , 44 – 50 , 55 , 56 , 59 , 60 , 63 ). Radiologists did not view AI as a threat to their professional roles or their autonomy, however, radiographers showed greater concern about AI undermining their job security ( 68 ). In our questionnaire, most disagreed that physicians will be replaced by AI in the future (68% disagreed).…”

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

“…While research related to radiologists' roles, clinical practise, and education in relation to AI has flourished, as discussed in the abovementioned paragraphs, very little research has considered the impact of AI on radiographers and their perception of using it in clinical practise. The limited literature available would suggest that radiographers are keen to engage with AI but controversy still exists whereby some radiographers feel that AI may deplete or threaten their jobs in the future whilst others think it may lead to more advanced role developments (31)(32)(33)(34). Abuzaid et al (35) surveyed the opinions of 34 radiologists and 119 radiographers in the UAE on their willingness to accept AI into practise.…”

Application of Machine Learning Methods to Improve the Performance of Ultrasound in Head and Neck Oncology: A Literature Review

“…It is therefore important to recognize that not all clinical tasks require or may benefit from the use of radiomics. In this regard, recent studies have reported on the knowledge and attitude of radiologists towards the clinical adoption of AI [56][57][58]. A survey of over 1000 radiologists revealed that a majority (77%) of participants had a positive attitude and were favorable towards the adoption of AI.…”

“…The perceived benefits of applying AI to routine radiologic practice reported by the participants included lower diagnostic error rates (73% of respondents) and optimization of radiologist workload (68% of respondents) while the risk of diminished professional reputation and increased costs, the higher workload for AI system maintenance and data analysis (39% of respondents) were the main concerns [56]. Similarly, a qualitative study by Chen et al observed that radiologists believe that AI has the potential to assist in performing repetitive tasks which in turn could allow them to focus on challenging clinical scenarios or reads [57]. The study also showed that the awareness and knowledge about AI greatly varied among professionals in radiology.…”

“…Developing a standardized framework for the education and training of medical professionals and trainees on ML/AI methods is, therefore, critical [59]. While the overall attitude and perception of AI among radiologists and radiation oncologists are generally positive [55][56][57][58]60], there is skepticism among physicians in other specialties (e.g., surgeons) on the claims of AI and concerns regarding the high risk of bias and lack of transparency in published studies [61,62]. It is therefore important to engage all stakeholders including radiologists, medical and radiation oncologists, surgeons, and patients during the development of AI studies to ensure meaningful and successful adoption of AI-based approaches in the clinical setting.…”