ArticleDigital health
Public patient views of artificial intelligence in healthcare: A nominal group technique study.
Article in Digital health. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers, 1 of them a synthesis that pooled it.
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Who cites it
25 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Barriers and Facilitators to Patient Acceptance of Artificial Intelligence in Health Care: Systematic Review.Journal of medical Internet research · 2026Pooled it
- Patients' experiences and attitudes toward the use of real-time artificial intelligence-assisted colonoscopy.Colorectal disease : the official journal of the Association of Coloproctology of Great Britain and Ireland · 2026Article
- The complementary roles of oversight, education, and collaboration in the responsible integration of artificial intelligence in radiation medicine: White paper of CADRA.Journal of applied clinical medical physics · 2026Review
- Artificial Intelligence in Public Health Education: A Scoping Review of Workforce Competency Development.Health science reports · 2026Article
- Key Information Influencing Patient Decision-Making About AI in Health Care: Survey Experiment Study.Journal of medical Internet research · 2026Article
- Patients' views on the use of artificial intelligence in healthcare: Artificial Intelligence Survey Aachen (AISA)-a prospective survey.Insights into imaging · 2026Article
- Machine Learning Applications in Population and Public Health: Guidelines for Development, Testing, and Implementation.JMIR public health and surveillance · 2025Article
- Understanding Public Judgements on Artificial Intelligence in Healthcare: Dialogue Group Findings From Australia.Health expectations : an international journal of public participation in health care and health policy · 2025Article
- Rheumatoid arthritis patients' perspective on the use of prediction models in clinical decision-making.Rheumatology (Oxford, England) · 2025Article
- How public discourse on medical AI shapes governance expectations: a Weibo-based mixed-methods study from China.Frontiers in public health · 2025Article
- Analysis of public perceptions on the use of artificial intelligence in genomic medicine.Human genomics · 2024Article
- Supporting long-term condition management: a workflow framework for the co-development and operationalization of machine learning models using electronic health record data insights.Frontiers in artificial intelligence · 2024Article
- Deep learning in optical coherence tomography: Where are the gaps?Clinical & experimental ophthalmology · 2023Review
- Towards Ideal Health Ecosystem With Artificial Intelligence-Driven Medical Services in India: An Overview.Cureus · 2023Review
- Patient perspectives on data sharing regarding implementing and using artificial intelligence in general practice - a qualitative study.BMC health services research · 2023Article
- Article
- Prioritization of surgical patients during the COVID-19 pandemic and beyond: A qualitative exploration of patients' perspectives.PloS one · 2023Article
- Perspectives on artificial intelligence in healthcare from a Patient and Public Involvement Panel in Japan: an exploratory study.Frontiers in digital health · 2023Article
- The ethics of advancing artificial intelligence in healthcare: analyzing ethical considerations for Japan's innovative AI hospital system.Frontiers in public health · 2023Article
- Review
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectivesThe beliefs of laypeople and medical professionals often diverge with regards to disease, and technology has had a positive impact on how research is conducted. Surprisingly, given the expanding worldwide funding and research into Artificial Intelligence (AI) applications in healthcare, there is a paucity of research exploring the public patient perspective on this technology. Our study sets out to address this knowledge gap, by applying the Nominal Group Technique (NGT) to explore patient public views on AI.
methodsA Nominal Group Technique (NGT) was used involving four study groups with seven participants in each group. This started with a silent generation of ideas regarding the benefits and concerns of AI in Healthcare. Then a group discussion and round-robin process were conducted until no new ideas were generated. Participants ranked their top five benefits and top five concerns regarding the use of AI in healthcare. A final group consensus was reached.
resultsTwenty-Eight participants were recruited with the mean age of 47 years. The top five benefits were: Faster health services, Greater accuracy in management, AI systems available 24/7, reducing workforce burden, and equality in healthcare decision making. The top five concerns were: Data cybersecurity, bias and quality of AI data, less human interaction, algorithm errors and responsibility, and limitation in technology.
conclusionThis is the first formal qualitative study exploring patient public views on the use of AI in healthcare, and highlights that there is a clear understanding of the potential benefits delivered by this technology. Greater patient public group involvement, and a strong regulatory framework is recommended.
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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.