{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,23]],"date-time":"2026-10-23T10:00:02Z","timestamp":1792749602089,"version":"4.5.0"},"reference-count":60,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,4,13]],"date-time":"2026-04-13T00:00:00Z","timestamp":1776038400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Digit. Health"],"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Artificial intelligence (AI) has the potential to revolutionize healthcare delivery in low- and middle-income countries (LMICs), yet its rapid adoption raises complex ethical, regulatory, and implementation challenges. This review investigates these barriers and identifies emerging strategies that support equitable and inclusive AI deployment in resource-limited settings.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>Following the PRISMA Extension for Scoping Reviews (PRISMA-ScR) guidelines, a systematic mapping of literature was conducted using PubMed, Scopus, and Cochrane Library (2000\u20132025) alongside global health policy reports. The search was framed using the Population, Concept, and Context (PCC) framework to identify studies addressing AI governance in LMICs. A total of 60 sources addressing ethical, regulatory, or implementation issues were analyzed across three domains derived from the WHO and OECD frameworks: governance, privacy, and AI applications.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>This study reveals that 7.4% of LMICs have adopted national AI strategies. Evidence indicates that over 60% of AI models in LMICs rely on non-representative datasets, increasing contextual bias. Of the 60 included studies, 25 focused on ethics, 17 on regulatory gaps, and 18 on implementation. Findings highlight workforce readiness gaps, with fewer than 10% of institutions offering structured AI training. Case studies from Brazil and India illustrate how these barriers are addressed through context-sensitive design.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>Successful AI integration requires context-sensitive design, participatory governance, and capacity building. This scoping review identifies critical gaps in empirical research on operationalization and recommends a transition from digital dependency to local innovation ecosystems.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fdgth.2026.1763884","type":"journal-article","created":{"date-parts":[[2026,4,13]],"date-time":"2026-04-13T05:48:42Z","timestamp":1776059322000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":10,"title":["Navigating ethical, regulatory, and implementation barriers to AI in healthcare: pathways toward inclusive digital health in low-resource settings\u2014a scoping review"],"prefix":"10.3389","volume":"8","author":[{"given":"Areeba","family":"Shahid","sequence":"first","affiliation":[{"name":"Department of Medicine and Surgery, Jinnah Sindh Medical University","place":["Karachi, Pakistan"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"F. N. 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