PhD Project - Understanding Patients Perceptions' of Artificial Intelligence use in Medical Imaging: A Mixed Methods Study
Applications for this PhD project are now open. The deadline for applications is 6 April 2026.
Artificial Intelligence (AI) is rapidly transforming healthcare, and nowhere is this more evident than in medical imaging. From supporting workflow prioritisation to enhancing image acquisition and interpretation, AI technologies are increasingly embedded across radiology pathways. Within the UK National Health Service (NHS), AI is being adopted to help manage rising demand, workforce shortages and reporting backlogs. While much research has examined the technical performance of AI tools, far less is known about how patients understand, experience and feel about these technologies and this gap presents an urgent opportunity for investigation.
This funded PhD project offers the chance to lead pioneering research at the intersection of medical imaging, AI ethics and patient experience. As imaging pathways often provide limited direct interaction between patients and practitioners, patients may be unaware of the extent to which AI contributes to their care. Emerging studies from the USA, Australia and Europe show that patients hold mixed views about AI: they recognise its potential benefits for accuracy and efficiency, yet remain concerned about depersonalisation, accountability, data privacy and the role of human expertise. However, there is currently very limited empirical research exploring these issues within the unique context of the UK NHS. Understanding UK patient perspectives is essential for ensuring that AI‑enhanced services are trustworthy, transparent and aligned with public expectations.
This PhD will explore UK patients’ perceptions, awareness and attitudes toward the use of AI in medical imaging. The project will employ an innovative sequential exploratory mixed‑methods design beginning with patient and public involvement activities to ensure relevance and co‑production. A systematic review will map existing evidence and identify conceptual and methodological gaps. You will then conduct qualitative interviews with a purposive sample of patients to explore experiences, concerns, trust and expectations in depth. Building on these insights, a large‑scale UK‑wide survey will be distributed across diagnostic imaging departments in multiple healthcare settings, enabling the identification of trends, group differences and factors influencing AI acceptance.
The successful candidate will develop expertise in a range of research methods including systematic review methodology, qualitative interviewing, thematic analysis and quantitative survey design and statistics. You will have the opportunity to work closely with NHS partners, patient representatives and interdisciplinary academic teams, ensuring that your work has real‑world relevance and impact.
This project will generate the first comprehensive UK evidence base on patient perceptions of AI in medical imaging. Findings will inform the development of patient‑centred communication strategies, contribute to policy discussions around AI deployment in the NHS, and support clinicians and service managers in implementing AI tools ethically and effectively. At a broader level, the research will help shape national conversations around digital transformation, patient trust and the future of AI‑enabled healthcare.
We welcome applications from candidates with backgrounds in radiography, or related fields who have a strong interest in AI, patient experience and health services research. This is an exceptional opportunity to contribute to a rapidly growing area and influence the responsible integration of AI within UK healthcare.
Project Contact:
Dr Olanrewaju Lawal
Email: olanrewaju.lawal@hsu.ac.uk
Details
This project aims to explore the perceptions of UK patients of AI use in medical imaging.
This study will utilise a sequential exploratory mixed methods design (Creswell & Clark, 2017). It will begin an initial public and patient involvement to inform the design and component of the project (Agyei‐Manu et al., 2019). Ethics approval will be sought from the Health Sciences University Ethics Committee and the NHS Research Ethics Committee. Further details of the components of the phases are described below.
Systematic review phase: This will examine existing evidence on patients’ perceptions, attitudes and experiences of artificial intelligence in medical imaging. Thus, establishing the current state of knowledge and methodological gaps and directly inform the design of the subsequent empirical phases.
Qualitative phase: Semi-structured interviews will be undertaken with purposive sample of patients (Silverman, 2021). The online interviews will explore patients’ perception in greater depth. Data analysed with the reflective thematic analysis (Braun & Clarke, 2021).
Quantitative phase: A cross-sectional survey (informed by the outcome of the initial two phases) of adult patients attending diagnostic imaging department within ten randomly selected NHS Trust within the UK (Wang & Cheng, 2020). The survey will include demographic data, prior explore to imaging, awareness of AI, levels of trust, and attitude towards AI-assisted imaging and reporting. We will utilise descriptive and inferential statistical analysis to identify trends and differences between groups (Sutanapong & Louangrath, 2015; Hazra, 2023).
The project will provide a novel understanding of patients’ perceptions of AI in medical imaging. In addition, it will identify key enablers and barriers to acceptance.
The project findings will inform the development of patient centred communication strategies that improve transparency around the use of AI in imaging, supporting informed consent and shared understanding. The study will generate evidence-based recommendations to guide clinicians, service managers and policy maker in implementing AI tools in ways that maintain patient trust and uphold professional accountability.
At a service level, insights from the research may inform the design of imaging pathways, patient information materials and staff training programmes, ensuring that AI adoption enhances rather than undermines the patient experiences. At national level, the findings will be relevant to NHS policy development, professional body guidance and ongoing workforce and digital transformation initiative aligned with the NHS Long Term Plan. Academic impact will be achieved through peer-reviewed publication, conference dissemination and knowledge exchange activities with clinical and patient stakeholders.
Funding 
HSU is offering up to three fee waivers for UK home applicants starting in October 2026. All eligible UK home applicants will automatically be considered for fee waiver support, which is awarded competitively based on the excellence of the candidate.
International applicants are unfortunately not eligible for fee waivers.
All applicants are expected to have financial plans in place to cover their studies and should not rely on a fee waiver.
Self-funded students are also welcome to apply for this project. Self-funded students can be UK home students or international students.
Availability
Available to both UK and International students.
Potential Supervisors
Dr Olanrewaju Lawal
Dr Simon Otter
Dr Victor ChigbunduNwaiwu
References
Agyei‐Manu, E., Atkins, N., Lee, B., Rostron, J., Dozier, M., Smith, M., & McQuillan, R. (2023). The benefits, challenges, and best practice for patient and public involvement in evidence synthesis: a systematic review and thematic synthesis. Health Expectations, 26(4), 1436-1452.
Aman, Z., & Qidwai, M. A. (2025). Self, Personality, AI, and Healthcare: Exploring the Intersection of Personalization and Ethical Boundaries. In Intersection of Human Rights and AI in Healthcare (pp. 69-98). IGI Global Scientific Publishing.
Braun, V., & Clarke, V. (2021). One size fits all? What counts as quality practice in (reflexive) thematic analysis?. Qualitative research in psychology, 18(3), 328-352..
Clements, W., Thong, L. P., Zia, A., Moriarty, H. K., & Goh, G. S. (2022). A prospective study assessing patient perception of the use of artificial intelligence in radiology. Asia Pacific Journal of Health Management, 17(1), 46-55.
Creswell, J. W., & Clark, V. L. P. (2017). Designing and conducting mixed methods research. Sage publications.
Hazra, A. (2023). Descriptive and inferential statistics in biomedical sciences: An overview. The quintessence of basic and clinical research and scientific publishing, 461-478.
McGhee, K. N., Barrett, D. J., Safarini, O., Elkassem, A. A., Eddins, J. T., Smith, A. D., & Rothenberg, S. A. (2025). Patient preferences for artificial intelligence in medical imaging: a single-center cross-sectional survey. Journal of Imaging Informatics in Medicine, 1-13.
Najjar, R. (2023). Redefining radiology: a review of artificial intelligence integration in medical imaging. Diagnostics, 13(17), 2760.
NHS England. (2019). Transforming imaging services in England: a national strategy for imaging networks. NHS Improvement publication code: CG, 51, 19.
Obuchowicz, R., Strzelecki, M., & Piórkowski, A. (2024). Clinical applications of artificial intelligence in medical imaging and image processing—A review. Cancers, 16(10), 1870.
Shelmerdine, S. C., Togher, D., Rickaby, S., & Dean, G. (2024). Artificial intelligence (AI) implementation within the national health service (NHS): the south west London AI working group experience. Clinical Radiology, 79(9), 665-672.
Silverman, D. (2021). Doing qualitative research.
Sutanapong, C., & Louangrath, P. I. (2015). Descriptive and inferential statistics. International Journal of Research & Methodology in Social Science, 1(1), 22-35.
Wang, X., & Cheng, Z. (2020). Cross-sectional studies: strengths, weaknesses, and recommendations. Chest, 158(1), S65-S71.
Williamson, S. M., & Prybutok, V. (2024). Balancing privacy and progress: a review of privacy challenges, systemic oversight, and patient perceptions in AI-driven healthcare. Applied Sciences, 14(2), 675.
Xuereb, F., & Portelli, J. L. (2024). The knowledge and perception of patients in Malta towards artificial intelligence in medical imaging. Journal of Medical Imaging and Radiation Sciences, 55(4), 101743.
Zhang, Z., Citardi, D., Wang, D., Genc, Y., Shan, J., & Fan, X. (2021). Patients’ perceptions of using artificial intelligence (AI)-based technology to comprehend radiology imaging data. Health informatics journal, 27(2), 14604582211011215.
Zheng, Q., Yang, L., Zeng, B., Li, J., Guo, K., Liang, Y., & Liao, G. (2021). Artificial intelligence performance in detecting tumor metastasis from medical radiology imaging: A systematic review and meta-analysis. EClinicalMedicine, 31.
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