City Research Online

Abstract

Introduction
Artificial Intelligence (AI) tools are increasingly used in imaging such as X-rays, but there is limited evidence about how patients view the use of AI in the diagnostic process.

Methods
Using the Technology Acceptance Model (TAM) to select attributes and the Health Belief Model (HBM) to interpret between-group differences this study evaluates patients' preferences regarding the use of AI in processing and communication of chest X-ray (CXR) results in the context of the NHS in the UK. A choice-based conjoint experimental (CBC) design was employed to examine various attributes influencing patient preferences across three samples 1) respondents with lung cancer diagnosis, 2) respondents who have had chest X-rays for any reason and 3) respondents with no recent CXRs. Furthermore, word–emotion association analysis was used to assess and compare the emotions levels in the open-text responses across the samples.

Results
A total of 440 respondents completed the study. Overall, respondents expressed support for using AI in X-ray processing, believing it would reduce waiting times. Waiting time and the decision on the next step of the care pathway accounted for the largest share of preference with a combined relative importance; the share of choice variation attributable to these attributes, of ∼67% in Samples 1 and 2 and 60% in Sample 3. Lung cancer patients (Sample 1) significantly favoured combined Human and AI processing (mean part-worth +13.3, p < 0.001), whereas Samples 2 and 3 favoured Human-only to Human and AI (mean part-worth −5.7, p = 0.008 and −12.0, p < 0.001 respectively for the attribute level Human and AI).

Conclusion
Overall, patients welcome the supplementary use of AI in their care, with lung cancer patients having higher acceptance.

Implications for practice
Understanding patient preferences can inform healthcare providers about optimising the diagnostic pathway, improving patient experiences, and potentially alleviating patient anxiety.

Publication Type:	Article
Additional Information:	© 2026 The Authors. Published by Elsevier Ltd on behalf of The College of Radiographers. This is an open access article distributed under the terms of the Creative Commons CC-BY license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Publisher Keywords:	Artificial intelligence, Lung cancer, Choice-based conjoint
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science R Medicine > RC Internal medicine R Medicine > RC Internal medicine > RC0254 Neoplasms. Tumors. Oncology (including Cancer)
Departments:	Bayes Business School Bayes Business School > Faculty of Management
SWORD Depositor:	Symplectic Administrator

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