ArticleEuropean radiology2020
Patients' views on the implementation of artificial intelligence in radiology: development and validation of a standardized questionnaire.
Article in European radiology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 81 papers, 3 of them syntheses that pooled it.
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Who cites it
81 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Barriers and Facilitators to Patient Acceptance of Artificial Intelligence in Health Care: Systematic Review.Journal of medical Internet research · 2026Pooled it
- Surveying Public Perceptions of Artificial Intelligence in Health Care in the United States: Systematic Review.Journal of medical Internet research · 2023Pooled it
- Noninvasive Determination ofAJNR. American journal of neuroradiology · 2021Pooled it
- Autonomous AI in prostate cancer: the road ahead towards clinical implementation.Abdominal radiology (New York) · 2026Review
- Towards equitable implementation of AI-assisted screening for diabetic retinopathy: Perspectives on digital literacy, trust and system readiness in developing countries.Diabetic medicine : a journal of the British Diabetic Association · 2026Article
- Augmenting medical data interpretation with Large Language Models (LLMs): a comparative analysis of patient empowerment, information processing, and technology acceptance.BMC medical informatics and decision making · 2026Article
- The complementary roles of oversight, education, and collaboration in the responsible integration of artificial intelligence in radiation medicine: White paper of CADRA.Journal of applied clinical medical physics · 2026Review
- Constructs Influencing Patient Perceptions of Use of AI in Medical Imaging Analysis: Systematic Review.Interactive journal of medical research · 2026Review
- Current patient attitudes to artificial intelligence applications in radiology.The British journal of radiology · 2026Article
- Renal Clinical Study Participants Support Data Sharing and Use of Artificial Intelligence.Kidney international reports · 2026Article
- Patient Preferences for Artificial Intelligence in Medical Imaging: A Single-Center Cross-Sectional Survey.Journal of imaging informatics in medicine · 2026Article
- Emerging trends in the early diagnosis of dental caries: a scoping review of artificial intelligence, digital diagnostics, and teledentistry.Evidence-based dentistry · 2026Article
- Anticipating Moral and Economic Considerations, Opportunities, and Potential Frictions for AI in Medical Imaging: Multistakeholder Cocreation Study.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
- Patients' views on the use of artificial intelligence in healthcare: Artificial Intelligence Survey Aachen (AISA)-a prospective survey.Insights into imaging · 2026Article
- Artificial intelligence and breast cancer screening in Serbia: a dual-perspective qualitative study among radiologists and screening-aged women.Frontiers in radiology · 2026Article
- Physician versus patient use of AI for diabetes prevention: public perceptions and comfort levels.Frontiers in digital health · 2026Article
- An Overview of Artificial Intelligence Applications in Radiological Imaging for Bone Fracture Diagnosis.Cureus · 2025Review
- Developing a Typology of Women's Attitudes Towards AI Use in the BreastScreen Programme-A Qualitative Study With BreastScreen Victoria Clients.Health expectations : an international journal of public participation in health care and health policy · 2025Article
- Parental and carer views on the use of AI in imaging for children: a national survey.Insights into imaging · 2025Article
21 more citing papers are in PubMed but not listed here.
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectivesThe patients' view on the implementation of artificial intelligence (AI) in radiology is still mainly unexplored territory. The aim of this article is to develop and validate a standardized patient questionnaire on the implementation of AI in radiology.
methodsSix domains derived from a previous qualitative study were used to develop a questionnaire, and cognitive interviews were used as pretest method. One hundred fifty-five patients scheduled for CT, MRI, and/or conventional radiography filled out the questionnaire. To find underlying latent variables, we used exploratory factor analysis with principal axis factoring and oblique promax rotation. Internal consistency of the factors was measured with Cronbach's alpha and composite reliability.
resultsThe exploratory factor analysis revealed five factors on AI in radiology: (1) distrust and accountability (overall, patients were moderately negative on this subject), (2) procedural knowledge (patients generally indicated the need for their active engagement), (3) personal interaction (overall, patients preferred personal interaction), (4) efficiency (overall, patients were ambiguous on this subject), and (5) being informed (overall, scores on these items were not outspoken within this factor). Internal consistency was good for three factors (1, 2, and 3), and acceptable for two (4 and 5).
conclusionsThis study yielded a viable questionnaire to measure acceptance among patients of the implementation of AI in radiology. Additional data collection with confirmatory factor analysis may provide further refinement of the scale. KEY POINTS: • Although AI systems are increasingly developed, not much is known about patients' views on AI in radiology. • Since it is important that newly developed questionnaires are adequately tested and validated, we did so for a questionnaire measuring patients' views on AI in radiology, revealing five factors. • Successful implementation of AI in radiology requires assessment of social factors such as subjective norms towards the technology.
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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.