The implementation of artificial intelligence in radiology: a narrative review of patient perspectives
Open accessAbstract
Aim: To synthesise research on the view of the public and patients of the use of artificial intelligence (AI) in radiology investigations. Methods: A literature review of narrative synthesis of qualitative and quantitative studies that reported views of the public and patients toward the use of AI in radiology. Results: Only seven studies related to patient and public views were retrieved, suggesting that this is an underexplored area of research. Two broad themes, of confidence in the capabilities of AI, and the accountability and transparency of AI, were identified. Conclusions: Both optimism and concerns were expressed by participants. Transparency in the implementation of AI, scientific validation, clear regulation and accountability were expected. Combined human and AI interpretation of imaging was strongly favoured over AI acting autonomously. The review highlights the limited engagement of the public in the adoption of AI in a radiology setting. Successful implementation of AI in this field will require demonstrating not only adequate accuracy of the technology, but also its acceptance by patients.
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Clinicians' awareness and knowledge of AI before using it contributed to its successful acceptance.
“Clinicians’ awareness and knowledge of AI before using it contributed to its successful acceptance [59,73].”
From Barriers to and facilitators of artificial intelligence adoption in health care: scoping review (2024) · cites this paper as [73]
The Discussion of the key themes (page unavailable) associates digital literacy and prior AI experience with greater acceptance, but it is describing patients, not clinicians. Strengths and limitations (page unavailable) says the observed influence of patients’ prior knowledge was modest. The review does not establish that clinicians’ knowledge before using AI contributed to their acceptance of it. (LLM, conf=0.86)
Experimental — accuracy not yet measuredLack of accountability in the decision-making process is a challenge.
“The lack of accountability in the decision-making process is a challenge; therefore, framing this in the governance model could be a way to address adoption issues related to accountability [37,50,73].”
From Barriers to and facilitators of artificial intelligence adoption in health care: scoping review (2024) · cites this paper as [73]
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- Authors
- Hemphill, S., Jackson, K., Bradley, S., & Bhartia, B.
- Affiliations
- Sir Charles Gairdner Hospital (AU)
- University of Leeds (GB)
- Leeds Teaching Hospitals NHS Trust (GB)
- Venue
- Future Healthcare Journal
- Publisher
- Elsevier BV
- Published
- 2023-03
- Volume
- 10
- Issue
- 1
- Pages
- 63-68
- ISSN
- 2514-6645 (ISSN-L)
- PMC
- PMC10538685
- OpenAlex
- W4362518315
- Cited by
- 16
- References
- 15
- Citations by year
- 2026: 6 · 2025: 5 · 2024: 4 · 2023: 1
- FWCI
- 0.19
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Future Healthcare Journal (publishedVersion, cc-by-nc-nd)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- White Rose Research Online (University of Leeds, The University of Sheffield, University of York) (submittedVersion)
- Topics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiology practices and education
- Physical Sciences › Computer Science › Artificial Intelligence › Explainable Artificial Intelligence (XAI)
- Keywords
- artificial intelligence in radiology, patient perspectives, public attitudes, trust in AI, accountability, transparency, human-AI collaboration, AI autonomy, AI regulation, artificial intelligence adoption, medical image interpretation
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