Artificial intelligence for healthcare in Nepal: A scoping review of clinical applications, implementation evidence, and health-system governance
TL;DR
Nepal’s AI-health landscape has progressed from conceptual awareness toward early diagnostic validation and deployment, particularly in imaging-based screening and medical education, yet the current evidence remains insufficient for conclusions about scale-up, safety, equity, cost-effectiveness, or system-wide impact.
Abstract
Background Nepal offers a distinctive LMIC setting for evaluating AI-health adoption due to its difficult geography, specialist shortage, high case burden needing screening and triage, and emerging digital-health policy reforms. Objective This scoping review aimed to synthesize existing literature on AI applications in medicine and healthcare in Nepal, including clinical use cases, education, implementation evidence, and health-system governance. Methods A scoping review was conducted following the Arksey–O’Malley framework and PRISMA-ScR reporting standards. Searches of PubMed, Embase, Scopus, and grey literature sources were conducted through May 2026. Sources addressing AI applications, implementation, education, or governance in Nepalese healthcare settings were included. Evidence was narratively synthesized, and implementation barriers were interpreted using the NASSS framework. Results Twenty-five sources were included. The literature showed transition from conceptual commentary toward empirical validation and early implementation studies. Imaging-based applications dominated, particularly in ophthalmology and chest radiography. Several AI systems demonstrated high diagnostic performance in selected datasets, including applications for retinopathy of prematurity, glaucoma, tuberculosis screening, and chest radiography. However, evidence was concentrated in urban or specialist settings, with no prospective implementation trials, limited rural validation, non-imaging clinical applications, health-economic analysis, and minimal governance evaluation. NASSS synthesis identified barriers related to local validation, accountability, workflow integration, regulatory uncertainty, and sustainability. Conclusion Nepal’s AI-health landscape has progressed from conceptual awareness toward early diagnostic validation and deployment, particularly in imaging-based screening and medical education. Yet, the current evidence remains insufficient for conclusions about scale-up, safety, equity, cost-effectiveness, or system-wide impact. Future studies should prioritize multi-site and rural validation, implementation studies, health-economic evaluation, non-imaging clinical applications, and phased AI-governance aligned with broader digital-health system development.