Qualitative systematic review identifies social-capital themes in healthcare AI implementation

A qualitative systematic literature review of 39 studies found that social capital was associated with several dimensions of healthcare artificial intelligence implementation, including professional networks, accountability arrangements, interpersonal and patient-provider trust, and shared norms. The review’s abstract reports that these factors shaped the introduction, sustainability, acceptability, ethical legitimacy, and practitioners’ willingness to integrate AI into clinical practice.

Researchers followed PRISMA reporting guidelines and searched PubMed, Web of Science, Embase, Cochrane, and CINAHL for literature published from January 2020 through April 2025. The search retrieved 14,703 records, from which 39 studies were included: 32 qualitative studies and seven mixed-methods studies. Methodological quality was assessed with the Joanna Briggs Institute Critical Appraisal Checklist for qualitative studies and the Mixed Methods Appraisal Tool. The review was registered in PROSPERO as CRD420250653624.

Thematic synthesis produced four overarching categories: physical structure, accountability structure, AI in social networks, and regulation and law. Physical structure and resource availability were interpreted as contextual antecedents rather than direct expressions of social capital. Networks connecting medical, technical, and administrative professionals, together with hierarchical accountability arrangements, were described as institutional channels through which AI was introduced and sustained. At the relational level, interpersonal trust between clinicians and AI tools, and patient-provider bonds reshaped by algorithmic mediation, were linked in the included literature to the acceptability and ethical legitimacy of adoption.

The synthesis further identified shared norms, collective attitudes, and value alignment around AI use as cognitive factors associated with how practitioners interpreted AI and their willingness to integrate it into clinical practice. Overall, the review characterized healthcare AI integration as a sociotechnical process involving interactions among infrastructure, accountability relationships, professional networks, trust, and shared norms. The evidence base consisted of qualitative and mixed-methods research, so the findings describe reported themes and relationships rather than establish causal effects or quantify the relative importance of each factor. The review primarily focused on healthcare providers and implementation actors, with attention also given to their accounts of relationships with wider stakeholders; this focus limits how completely the synthesis represents all healthcare stakeholders. Because the supplied source provides the review abstract rather than the full article, the findings above are based on the abstract’s summary of the review’s methods and results.

Subject of Research: Social-capital factors in healthcare artificial intelligence implementation

Article Title: Social capital factors in healthcare AI implementation: a qualitative systematic review

Article References: Pang, R., Xu, Y., Wu, Z., Wang, J., Yang, Z., Zhang, X., Li, H., Chen, N., & Pan, H. (2026). Social capital factors in healthcare AI implementation: a qualitative systematic review. BMC Health Services Research. https://doi.org/10.1186/s12913-026-15742-1

Image Credits: AI Generated

DOI: 10.1186/s12913-026-15742-1

Keywords: healthcare artificial intelligence, social capital, systematic review, qualitative research, implementation, professional networks, trust, accountability, shared norms, Social, capital, factors