SynthesisBMC primary care2026
Adoption of artificial intelligence in primary health care: systematic synthesis of stakeholder perspectives.
Synthesis in BMC primary care, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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
2 citing papers in PubMed.
- Applications of Artificial Intelligence in the Health Sector: A PRISMA-Based Systematic Review.Healthcare (Basel, Switzerland) · 2026Review
- Human-AI Interaction in Interventional Radiology: A Narrative Review of Current Applications, Challenges, and Future Directions.Journal of imaging · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
introductionPrimary care, the cornerstone of healthcare systems, faces increasing pressures from aging populations, chronic diseases, and resource constraints. Artificial intelligence (AI) offers transformative potential to enhance diagnostic accuracy, streamline workflows, and improve patient outcomes. However, its integration into primary care is challenged by technical, ethical, and organizational barriers. This systematic review examines AI’s role in primary care, focusing on stakeholder perspectives and implementation dynamics.
methodsA systematic synthesis of qualitative studies was conducted following Noblit and Hare’s framework and Braun and Clarke’s thematic analysis. Searches spanned PubMed, Scopus, Web of Science, CINAHL, and grey literature (2015–2025), identifying qualitative studies on AI in primary care. Studies were screened using predefined criteria, with quality assessed via the Critical Appraisal Skills Programme (CASP) checklist. Data were extracted systematically and synthesized, with initial search for 1416 studies.
resultsFinally, 23 studies from diverse regions (e.g., UK, USA, Australia, Cameroon) and involving stakeholders like physicians, patients, and policymakers were included. Six themes emerged: Barriers (technical, organizational, policy, knowledge, cultural), Facilitators (benefits, trust, support systems, evidence), Impact on Healthcare Delivery (workflow, decision-making, roles, engagement), Ethical/Legal/Social Implications (privacy, accountability, equity, public perception), Stakeholder Perspectives, and Future Directions. AI improved efficiency and diagnostics but faced challenges like data quality, trust deficits, and ethical concerns.
conclusionAI holds significant promise for transforming primary care by enhancing efficiency and patient care, but its adoption is hindered by multifaceted barriers from stakeholder perspectives. Transparent AI systems, robust training, and ethical frameworks are crucial to build trust and ensure equity. Future research should focus on longitudinal impacts and inclusive strategies to align AI with primary care’s patient-centered ethos.
Indexed as
Identifiers
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.