ArticleBMC health services research2021
Professionals' responses to the introduction of AI innovations in radiology and their implications for future adoption: a qualitative study.
Article in BMC health services research, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 43 papers, 3 of them syntheses that pooled it.
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Who cites it
43 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Healthcare Professionals' Perceptions of Artificial Intelligence in Healthcare-A Systematic Review of Qualitative Studies.Journal of advanced nursing · 2026Pooled it
- Ethical implications of the use of AI-based technologies for medical image classification systems in screening: a qualitative systematic review.Health technology assessment (Winchester, England) · 2026Pooled it
- Healthcare professionals' perspectives on artificial intelligence in patient care: a systematic review of hindering and facilitating factors on different levels.BMC health services research · 2025Pooled it
- From resistance to reliance: A human-centered analysis of the spectrum of radiologists' trust in AI.European journal of radiology open · 2026Article
- Healthcare Providers' Perspectives on the Role of Artificial Intelligence in the Care of Patients with Inflammatory Bowel Disease: An International Survey.Digestive diseases and sciences · 2026Article
- Patient and staff acceptability of an artificial intelligence software to prioritize chest X-rays in an NHS setting: a qualitative evaluation.BMC health services research · 2026Article
- Employee perceptions of AI adoption across service domains in a Finnish public health and social care organization: a cross-sectional mixed-methods study.BMC health services research · 2026Article
- Investigating the Potential of Generative AI Clinical Case-Based Simulations on Radiography Education: A Pilot Study.Journal of imaging informatics in medicine · 2026Article
- Article
- Implementing an Artificial Intelligence Decision Support System in Radiology: Prospective Qualitative Evaluation Study Using the Nonadoption Abandonment Scale-Up, Spread, and Sustainability (NASSS) Framework.Journal of medical Internet research · 2026Article
- Ethical Knowledge, Challenges, and Institutional Strategies Among Medical AI Developers and Researchers: Focus Group Study.Journal of medical Internet research · 2026Article
- Implementing AI innovation in radiology departments in the English NHS: a qualitative study on the experiences of professionals, patient groups and innovators.Frontiers in digital health · 2026Article
- Translating AI to the Bedside with Physician Buy-In: Recommendations from a Meta-Analysis and Systematic Review of the Literature.Bioengineering (Basel, Switzerland) · 2025Review
- Responsible Adoption of Artificial Intelligence (AI) in Pharmacy Practice: Perspectives of Regulators in Canada and the United States.Pharmacy (Basel, Switzerland) · 2025Article
- Ethical Pitfalls in AI-Based Predictive Models in Surgery.World journal of surgery · 2025Review
- How Could Artificial Intelligence Change the Doctor-Patient Relationship? A Medical Ethics Perspective.Healthcare (Basel, Switzerland) · 2025Review
- Seven Opportunities for Artificial Intelligence in Primary Care Electronic Visits: Qualitative Study of Staff and Patient Views.Annals of family medicine · 2025Article
- Artificial intelligence for diagnostics in radiology practice: a rapid systematic scoping review.EClinicalMedicine · 2025Article
- Studying the Potential Effects of Artificial Intelligence on Physician Autonomy: Scoping Review.JMIR AI · 2025Review
- Enhancing Diagnostic Accuracy of Lung Nodules in Chest Computed Tomography Using Artificial Intelligence: Retrospective Analysis.Journal of medical Internet research · 2025Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundArtificial 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.
methodsWe 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.
resultsWe 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.
conclusionsIn 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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.