AI-Enabled Healthcare Systems: A Scoping Review of Socio-Technical, Governance, and Implementation Challenges

Abstract

Artificial intelligence (AI) is embedded in healthcare through decision support, imaging, documentation, monitoring, digital twins, and smart-hospital infrastructures. This scoping review mapped technologies, healthcare contexts, socio-technical dimensions, governance mechanisms, and implementation conditions of AI-enabled healthcare systems. The review followed PRISMA-ScR. Scopus, Web of Science Core Collection, PubMed, and IEEE Xplore were searched on 1 July 2026 for English-language sources published from 2021 to 2026. All four authors participated in source selection; each record was assessed by two reviewers, and disagreements were resolved by consensus. Data were charted in matrices and synthesized descriptively and thematically. Of 2422 records, 426 duplicates were removed and 1996 were screened. Among 185 full-text reports, 124 were excluded, including 18 for insufficient methodological or empirical information, and 61 were included. Included sources then underwent a complementary seven-criterion cross-design appraisal scored from 1 to 3, without altering the final corpus. Technologies included machine learning, deep learning, decision support, explainable AI, natural language processing, large language models, interoperability frameworks, blockchain/IoMT, and digital twins. Challenges involved validation, data quality, interoperability, accountability, privacy, security, explainability, trust, bias, equity, and workforce readiness. Reported implementation facilitators included interoperable infrastructure, participatory design, lifecycle governance, continuous validation, and context-sensitive implementation.