SynthesisBMC health services research2025
Healthcare professionals' perspectives on artificial intelligence in patient care: a systematic review of hindering and facilitating factors on different levels.
Synthesis in BMC health services research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers, 2 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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
28 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Artificial intelligence adoption in healthcare: a systematic review of implementation challenges and health services implications.BMC health services research · 2026Pooled it
- Adoption of artificial intelligence in primary health care: systematic synthesis of stakeholder perspectives.BMC primary care · 2026Pooled it
- Patients' and Physicians' Perceptions of AI Integration in Prostate Cancer Diagnosis: Mixed Methods Study of Challenges to the Patient-Physician Relationship.Journal of medical Internet research · 2026Article
- Article
- The Perceived Influence of AI on the Craftsmanship of Physicians: Qualitative Interview and Focus Group Study.JMIR AI · 2026Article
- Clinicians' Attitudes and Perceptions on the Adoption of AI in Mental Health Care: Scoping Review.Journal of medical Internet research · 2026Article
- Artificial Intelligence as an Add-On Instrument in Fetal Ultrasound; Sonographers' and Obstetricians' Expectations.Prenatal diagnosis · 2026Article
- 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
- Use, Concerns, and Perspectives on AI in Health Care Among French Health Professionals and Students: Web-Based Cross-Sectional Survey.JMIR medical education · 2026Article
- The role of health literacy and attitudes toward artificial intelligence in the acceptance of telemedicine services among adults in Turkey: a cross-sectional study.BMC primary care · 2026Article
- From technological optimism to clinical realism: medical students' attitudes toward artificial ıntelligence and ıts career ımplications - a cross-sectional study at a Turkish Medical Faculty.BMC medical education · 2026Article
- Artificial Intelligence Legislation Literacy, Governance Readiness, and Adoption Intentions in Romanian Healthcare: A Cross-Sectional Study.Healthcare (Basel, Switzerland) · 2026Article
- Explainability and Human Oversight for AI-Generated Exercise Guidance in Digital Healthcare: A Governance-Oriented Narrative Review.Healthcare (Basel, Switzerland) · 2026Review
- Smart Technology, Fragile Hearts: Navigating AI's Challenges and Limitations in Heart Failure Management.Current heart failure reports · 2026Review
- Employee perceptions of AI adoption across service domains in a Finnish public health and social care organization: a cross-sectional mixed-methods study.BMC health services research · 2026Article
- Attitudes, Needs, and Expectations Regarding the Application of AI in Occupational Healthcare: A Multiple Stakeholder Perspective.Journal of occupational and environmental medicine · 2026Article
- Artificial intelligence adoption in French cardiovascular care: a multiprofessional survey of barriers and facilitators.European heart journal. Digital health · 2026Article
- Healthcare professionals' perspectives on the utility of chronic postsurgical pain prediction profiles in perioperative care: a qualitative study.Journal of anesthesia, analgesia and critical care · 2026Article
- Patient and dental practitioner acceptance of artificial intelligence in dental care: a cross-sectional study in Saudi Arabia's eastern province.Frontiers in oral health · 2026Article
- The anatomy of AI implementation skepticism in Polish healthcare: an explanatory mixed-methods analysis of psychographic barriers among healthcare professionals.Frontiers in public health · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
14 authors.
Funding
Abstract
backgroundArtificial intelligence (AI) applications present opportunities to enhance the diagnosis, prognosis, and treatment of various diseases. To successfully integrate and utilize AI in healthcare, it is crucial to understand the perspectives of healthcare professionals and to address challenges they associate with AI adoption at an early stage. Therefore, the aim of this review is to provide a comprehensive overview of empirical studies that explore healthcare professionals' perspectives on AI in healthcare.
methodsThe review was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses framework. The databases MEDLINE, PsycINFO, and Web of Science were searched in the timeline of 2017 to 2024 using terms related to 'healthcare professionals', 'artificial intelligence', and 'perspectives'. Eligible were peer-reviewed articles that employed quantitative, qualitative, or mixed-methods approaches. Extracted facilitating and hindering factors were analysed according to the dimensions of the socio-ecological model.
resultsOur search yielded 4,499 articles published up to February 2024. After title abstract screening, 150 full-texts were assessed for eligibility, and 72 studies were ultimately included in our synthesis. The extracted perspectives on AI were thematically analyzed using the socioecological model in order to identify various levels of influence and to categorize them into facilitating and hindering factors. In total, we identified 49 facilitating and 43 hindering factors across all levels of the socioecological model.
conclusionsThe findings from this review can serve as a foundation for developing guidelines for AI implementation adressing various stakeholders, from healthcare professionals to policymakers. Future research should focus on the empirical adoption of AI applications and, if possible, further examine the hindering factors associated with different types of AI.
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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.