ArticleJournal of medical radiation sciences2026
Preliminary Clinical Evaluation in Cross-Sectional Imaging: A Qualitative Inquiry Into the Ethical Considerations in the Era of Artificial Intelligence.
Article in Journal of medical radiation sciences, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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3 authors.
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Abstract
introductionThe evolving role of radiographers in Preliminary Clinical Evaluation (PCE), alongside the integration of artificial intelligence (AI) into radiography workflows, is reshaping professional boundaries and enhancing patient care. However, this introduces ethical challenges that may impact PCE performance. This study explored the perspectives of cross-sectional imaging radiographers on the ethical dimensions of PCE and the potential impact of AI on the future practice of PCE.
methodA descriptive qualitative inquiry employing semi-structured interviews was conducted. A Purposive sampling method was used to recruit twelve cross-sectional imaging radiographers in a postgraduate medical imaging course at a UK university. Data were analysed using inductive reflexive thematic analysis with NVivo (v.20.0).
resultsParticipants' clinical experience ranged (2 to 8 years), with 50% (n = 6) employed in public hospitals and the rest in private or mixed settings. Half (n = 6) had expertise in both CT/MRI and the rest in CT only. Notably, 42% (n = 5) practised in multiple countries. Two main themes were developed: (1) Ethical considerations of PCE with subthemes surrounding privacy and confidentiality, communication boundaries, and legal compliance. (2) Prospects of human-centred PCE in the era of AI, with subthemes including augmentation and support, workflow efficiency, PCE validation and quality assurance.
conclusionParticipants shared relevant ethical principles underpinning PCE practice and are cautiously optimistic about the potential impact of AI on PCE. Incorporation of PCE-specific ethics into radiography education and practice is necessary to ensure consistency, accountability and safety. Cautious integration of AI into radiography practice workflows is recommended to avoid professional deskilling and to preserve expertise.
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