ArticleDigital health
Ethical and privacy challenges of integrating generative AI into EHR systems in Tanzania: A scoping review with a policy perspective.
Article in Digital health. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- Artificial intelligence approaches to predicting treatment non-adherence in chronic diseases: a narrative review.Frontiers in digital health · 2026Review
- Digital healthcare services in the context of age-friendly community initiatives in China: a realist evaluation.BMC health services research · 2025Article
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Authors and funding
1 author.
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Abstract
Objectives: This study examines the ethical and privacy challenges of integrating generative artificial intelligence (AI) into electronic health record (EHR) systems, focusing on Tanzania's healthcare context. It critically analyses the extent to which Tanzania's Policy Framework for Artificial Intelligence in the Health Sector (2022) addresses these challenges and proposes regulatory and practical safeguards for responsible generative AI deployment. Methods: A systematic scoping review was conducted using PubMed, IEEE Xplore, Scopus and Google Scholar to identify relevant studies published between 2014 and 2024. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) guidelines informed the search and selection process. Fourteen studies met the inclusion criteria and were thematically analysed to identify key ethical and privacy concerns of generative AI in healthcare. Moreover, a policy analysis of Tanzania's AI framework was conducted to assess its alignment with global best practices and regulatory preparedness. Results: The review identified six key ethical and privacy challenges associated with generative AI in EHR systems: data privacy and security risks, algorithmic bias and fairness concerns, transparency and accountability issues, consent and autonomy challenges, human oversight gaps and risks of data re-identification. The policy analysis revealed that while Tanzania's AI framework aligns with national health priorities and promotes capacity building and ethical governance, it lacks generative AI-specific guidelines, regulatory clarity and resource mobilisation strategies necessary for healthcare settings. Conclusion: Integrating generative AI into Tanzania's EHR systems presents transformative opportunities and significant ethical and privacy risks. Tanzania's policy framework should incorporate AI-specific ethical guidelines, operationalise regulatory mechanisms, foster stakeholder engagement through participatory co-design and strengthen infrastructural investments. These measures will promote ethical integrity, enhance patient trust and position Tanzania as a regional leader in responsible AI use in healthcare.
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