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Introduction <p>Primary 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.</p> Methods <p>A 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.</p> Results <p>Finally, 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.</p> Conclusion <p>AI 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.</p>

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Adoption of artificial intelligence in primary health care: systematic synthesis of stakeholder perspectives

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年1月7日

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  • Hadi Sabat Sani,
  • Masoumeh Mobaraki,
  • Atefeh Sholibor,
  • Somayeh Fallah,
  • Fatemeh Abdi,
  • Mohammad Hasannezhad,
  • Mohammadamin Jandaghian-Bidgoli

摘要

Introduction

Primary 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.

Methods

A 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.

Results

Finally, 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.

Conclusion

AI 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.