ArticleBMC health services research2023
Exploring patient perspectives on how they can and should be engaged in the development of artificial intelligence (AI) applications in health care.
Article in BMC health services research, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 42 papers, 3 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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
42 citing papers in PubMed, 3 syntheses or guidelines pooled it, 77 citations in OpenAlex.
- Artificial intelligence and patient reported outcomes in ophthalmology.Journal of patient-reported outcomes · 2026Pooled it
- Families' Experiences With Family-Focused Web-Based Interventions for Improving Health: Qualitative Systematic Literature Review.Journal of medical Internet research · 2025Pooled it
- Smartphone Apps for Pulmonary Hypertension: Systematic Search and Content Evaluation.JMIR mHealth and uHealth · 2024Pooled it
- Patients' Perspectives on Applications of AI and Personalized Medicine in Life-Threatening Heart Disease: European Cross-Sectional Patient Questionnaire Study.Journal of medical Internet research · 2026Article
- Challenges of AI for cost-effectiveness in health: a hybrid MCDM model.Health economics review · 2026Article
- Using Arts-Based Methods to Involve People Living in Tower Hamlets With Multiple Long-Term Conditions in the Development of Artificial Intelligence Tools in Healthcare Research.Health expectations : an international journal of public participation in health care and health policy · 2026Article
- Patient Perspectives on the Use of Artificial Intelligence in Malignant Melanoma Diagnosis.Dermatology practical & conceptual · 2026Article
- Patient Preferences for Artificial Intelligence in Medical Imaging: A Single-Center Cross-Sectional Survey.Journal of imaging informatics in medicine · 2026Article
- Where Can Artificial Intelligence Assist Cancer Care?: Examining Patient-Centered Communication Dimension Effects.Health services research · 2026Article
- Understanding Responsible Development in AI-Based Clinical Prediction Models for Mortality: Protocol for a Scoping Review.JMIR research protocols · 2026Article
- The potential of artificial intelligence in clinical trials.European journal of clinical investigation · 2026Review
- Artificial intelligence in rural health care: A framework for responsible adoption, readiness, and access.The Journal of rural health : official journal of the American Rural Health Association and the National Rural Health Care Association · 2026Article
- The patient matters: a roundtable discussion on pathology in the era of digitization and AI.Virchows Archiv : an international journal of pathology · 2026Article
- Evidence for Digital Health Tools Designed to Support the Triage of Musculoskeletal Conditions in Primary, Urgent, and Emergency Care Settings: Scoping Review.Journal of medical Internet research · 2026Article
- Key Information Influencing Patient Decision-Making About AI in Health Care: Survey Experiment Study.Journal of medical Internet research · 2026Article
- Operationalising Inclusion for Participatory Design: Worked Examples for TRIPOD+AI & PROBAST+AI.Health services insights · 2026Article
- A review for navigating the trade-offs: evaluating open-source and proprietary large language models for clinical and biomedical information extraction.Frontiers in digital health · 2026Review
- Engaging patients in AI governance.BMJ digital health & AI · 2026Article
- Biomedical data repositories require governance for artificial intelligence/machine learning applications at every step.JAMIA open · 2025Article
- Ethical considerations of artificial intelligence in emergency medicine for triage and resource allocation: a scoping review.Clinical and experimental emergency medicine · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundArtificial intelligence (AI) is a rapidly evolving field which will have implications on both individual patient care and the health care system. There are many benefits to the integration of AI into health care, such as predicting acute conditions and enhancing diagnostic capabilities. Despite these benefits potential harms include algorithmic bias, inadequate consent processes, and implications on the patient-provider relationship. One tool to address patients' needs and prevent the negative implications of AI is through patient engagement. As it currently stands, patients have infrequently been involved in AI application development for patient care delivery. Furthermore, we are unaware of any frameworks or recommendations specifically addressing patient engagement within the field of AI in health care.
methodsWe conducted four virtual focus groups with thirty patient participants to understand of how patients can and should be meaningfully engaged within the field of AI development in health care. Participants completed an educational module on the fundamentals of AI prior to participating in this study. Focus groups were analyzed using qualitative content analysis.
resultsWe found that participants in our study wanted to be engaged at the problem-identification stages using multiple methods such as surveys and interviews. Participants preferred that recruitment methodologies for patient engagement included both in-person and social media-based approaches with an emphasis on varying language modalities of recruitment to reflect diverse demographics. Patients prioritized the inclusion of underrepresented participant populations, longitudinal relationship building, accessibility, and interdisciplinary involvement of other stakeholders in AI development. We found that AI education is a critical step to enable meaningful patient engagement within this field. We have curated recommendations into a framework for the field to learn from and implement in future development.
conclusionGiven the novelty and speed at which AI innovation is progressing in health care, patient engagement should be the gold standard for application development. Our proposed recommendations seek to enable patient-centered AI application development in health care. Future research must be conducted to evaluate the effectiveness of patient engagement in AI application development to ensure that both AI application development and patient engagement are done rigorously, efficiently, and meaningfully.
Indexed as
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What OpenQuestion holds
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.