Mapping the landscape of AI in healthcare in Kazakhstan: a scoping review of readiness, development, and adoption

Abstract

Abstract Background The rapid development of artificial intelligence (AI) and machine learning (ML) technologies has created new opportunities for improving healthcare systems worldwide. In Kazakhstan, national digitalization initiatives have supported the introduction of electronic health records and medical information systems; however, the level of AI readiness, development, and real-world implementation in healthcare remains insufficiently explored. Objective This study aimed to synthesize current evidence on the readiness, development, and implementation of AI and ML technologies in the healthcare sector of Kazakhstan. Methods A scoping review was conducted following the Arksey and O’Malley methodological framework and reported according to the PRISMA-ScR guidelines. The protocol of this scoping review was registered on the Open Science Framework (OSF). Electronic searches were performed in PubMed, Scopus, Web of Science, ScienceDirect, and Google Scholar for studies published between 2020 and 2025. Inclusion criteria covered empirical studies, policy analyses, and mixed-methods research focusing on AI in Kazakhstan’s healthcare context. Data were extracted using a standardized template and synthesized across three domains: AI readiness, AI development, and AI implementation. Results A total of ten studies were included in the final synthesis. Evidence of AI readiness was mainly reported at the educational, professional, regulatory, and system levels, with limited workforce preparedness, insufficient formal training, and gaps in data protection and ethical regulation. AI development was primarily concentrated on technical model creation, including deep learning for medical imaging and automated laboratory interpretation, but often lacked clinical validation. Real-world AI implementation was reported in a small number of clinical settings, particularly in rehabilitation and laboratory medicine, where AI tools were reported to improve workflow efficiency, diagnostic support, and documentation processes. Conclusions The Kazakhstan AI/ML healthcare literature is emerging and heterogeneous. While technical development and foundational digital infrastructure are advancing, evidence of routine clinical implementation remains limited, and readiness gaps persist in training, governance, interoperability, and regulatory oversight. Future research should prioritize implementation-focused evaluations, clinical validation, and governance models to support safe adoption across healthcare settings. Clinical trial number Not applicable.

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