ArticleBMJ open2023
Experiences of using artificial intelligence in healthcare: a qualitative study of UK clinician and key stakeholder perspectives.
Article in BMJ open, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT05028179 (PROTEUS), which is not on this map. Cited by 29 papers, 2 of them syntheses that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
PROTEUS: A PROspective Randomised Controlled Trial Evaluating the Use of Artificial Intelligence in Stress Echocardiology
Who cites it
29 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Ethical and practical challenges of generative AI in healthcare and proposed solutions: a survey.Frontiers in digital health · 2025Pooled it
- Impact of generative AI in medical education in India: a systematic review.Frontiers in artificial intelligence · 2025Pooled it
- Trial
- Anticipated Barriers and Facilitators to the Implementation of an AI Tool Advising on the Placement of Mitigation Strategies to Improve Air Quality in the Hospital Setting: Qualitative Study.JMIR human factors · 2026Article
- Use of Direct-to-Consumer Telemedicine: A Qualitative Study Exploring the Thoughts, Perspectives and Experiences of Australian Adults.Health expectations : an international journal of public participation in health care and health policy · 2026Article
- Primary Care Doctors' Perspectives and Experiences With a Chest X-Ray AI Triage Program: Qualitative Study.Journal of medical Internet research · 2026Article
- Digital Health Competence and Attitudes Toward AI Among Health Care Professionals: Convergent Mixed Methods Study.Journal of medical Internet research · 2026Article
- The Design-Driven Innovation Path of Human-Centered Artificial Intelligence in the Field of Healthcare: Theory, Practice, and Future Prospects.Healthcare (Basel, Switzerland) · 2026Review
- Article
- Patient perspectives about deployment of artificial intelligence decision support tools in a safety-net healthcare system.JAMIA open · 2026Article
- "Black box" artificial intelligence for mortality prediction: a mixed-methods study of palliative care team, patient, and caregiver perspectives.Annals of palliative medicine · 2026Article
- How Do Radiologists Currently Monitor AI in Radiology and What Challenges Do They Face? An Interview Study and Qualitative Analysis.Journal of imaging informatics in medicine · 2026Article
- Use of artificial intelligence in the out-of-hospital care settings: a scoping review.BMJ digital health & AI · 2026Article
- Digital Innovations in Nursing Workforce Management: A Scoping Review of Advanced Applications and Implementation Challenges.Journal of nursing management · 2026Article
- Advocating for trust in and trustworthy AI to transform evidence synthesis.Environmental evidence · 2025Article
- Evaluating Conferences as a Tool to Improve Medical Artificial Intelligence Comprehension among Healthcare Professionals: A before and after Study.JMA journal · 2025Article
- Exploring doctors' perspectives on generative-AI and diagnostic-decision-support systems.BMJ health & care informatics · 2025Article
- Exploring Undergraduate Medical Students' Perspectives towards Artificial Intelligence in Healthcare: A Qualitative Study from India.JMA journal · 2025Article
- Evaluating accountability, transparency, and bias in AI-assisted healthcare decision- making: a qualitative study of healthcare professionals' perspectives in the UK.BMC medical ethics · 2025Article
- Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
Abstract
objectivesArtificial intelligence (AI) is a rapidly developing field in healthcare, with tools being developed across various specialties to support healthcare professionals and reduce workloads. It is important to understand the experiences of professionals working in healthcare to ensure that future AI tools are acceptable and effectively implemented. The aim of this study was to gain an in-depth understanding of the experiences and perceptions of UK healthcare workers and other key stakeholders about the use of AI in the National Health Service (NHS).
designA qualitative study using semistructured interviews conducted remotely via MS Teams. Thematic analysis was carried out.
settingNHS and UK higher education institutes.
participantsThirteen participants were recruited, including clinical and non-clinical participants working for the NHS and researchers working to develop AI tools for healthcare settings.
resultsFour core themes were identified: positive perceptions of AI; potential barriers to using AI in healthcare; concerns regarding AI use and steps needed to ensure the acceptability of future AI tools. Overall, we found that those working in healthcare were generally open to the use of AI and expected it to have many benefits for patients and facilitate access to care. However, concerns were raised regarding the security of patient data, the potential for misdiagnosis and that AI could increase the burden on already strained healthcare staff.
conclusionThis study found that healthcare staff are willing to engage with AI research and incorporate AI tools into care pathways. Going forward, the NHS and AI developers will need to collaborate closely to ensure that future tools are suitable for their intended use and do not negatively impact workloads or patient trust. Future AI studies should continue to incorporate the views of key stakeholders to improve tool acceptability. TRIAL REGISTRATION NUMBER: NCT05028179; ISRCTN15113915; IRAS ref: 293515.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.