Patients’ views on the implementation of artificial intelligence in radiology: development and validation of a standardized questionnaire
Open accessAbstract
Objectives: The 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. Methods: Six 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. Results: The 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). Conclusions: This 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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Lack of accuracy, doubts about unsafe results, privacy breaches, patients' perceptions and acceptance of automation, and uncertainty about developers' reliability, availability, usability, and perceived usefulness were found to be obstacles to gaining trust.
“Lack of accuracy; doubts about unsafe results; privacy breaches; patients’ perceptions and acceptance of automation; and uncertainty about developers’ reliability, availability, usability, and perceived usefulness were found to be obstacles to gaining trust [39,42,47,50,53,54,69,70].”
From Barriers to and facilitators of artificial intelligence adoption in health care: scoping review (2024) · cites this paper as [69]
The Results (‘Patients’ views on AI in radiology,’ page not specified) report moderate distrust concerning diagnostic accuracy, communication, and confidentiality; the questionnaire also records worries about scans analyzed without human involvement and personal data falling into the wrong hands. These support concerns about accuracy, privacy, and acceptance of automation, but the paper does not report actual privacy breaches or identify developers’ reliability, availability, usability, and perceived usefulness as obstacles to trust. (LLM, conf=0.88)
Experimental — accuracy not yet measuredThe increased anxiety can potentially be attributed to the perception of currently available AI as premature technology.
“This anxiety can potentially be attributed to the perception of currently available AI as premature technology,26,30–33 which could be addressed by providing more information about its effectiveness and exact application in relation to the AI tool.”
From Patient perspectives on informed consent for medical AI: A web-based experiment (2024) · cites this paper as [30]
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References 24 · Cited by 161
- Authors
- Ongena, Y. P., Haan, M., Yakar, D., & Kwee, T. C.
- Affiliations
- University of Groningen (NL)
- University Medical Center Groningen (NL)
- Corresponding author
- Ongena, Yfke P.
- Venue
- European Radiology
- Published
- 2019-11-08
- Volume
- 30
- Issue
- 2
- Pages
- 1033-1040
- ISSN
- 0938-7994 (ISSN-L)
- 1432-1084
- PMC
- PMC6957541
- OpenAlex
- W2986199283
- Cited by
- 161
- References
- 24
- Citations by year
- 2026: 25 · 2025: 25 · 2024: 34 · 2023: 30 · 2022: 26 · 2021: 12 · 2020: 8 · 2019: 1
- FWCI
- 3.73
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Locations
- European Radiology (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- University of Groningen research database (University of Groningen / Centre for Information Technology) (publishedVersion, cc-by)
- University of Groningen research database (University of Groningen / Centre for Information Technology) (submittedVersion, cc-by)
- Funders
- University of Groningen
- Topics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiology practices and education
- Health Sciences › Health Professions › General Health Professions › Patient-Provider Communication in Healthcare
- Keywords
- artificial intelligence in radiology, patient acceptance, questionnaire validation, procedural knowledge, personal interaction, efficiency, patient information, subjective norms, AI implementation
- MeSH terms
- Adolescent, Adult, Aged, Aged, 80 and over, Artificial Intelligence, Attitude to Computers, Attitude to Health, Humans, Male, Middle Aged, Netherlands, Prospective Studies, Psychometrics, Surveys and Questionnaires, Radiography, Radiology, Reproducibility of Results, Young Adult
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