The use of artificial intelligence-based innovations in the health sector in Tanzania: A scoping review. Issue 1 (March 2023)

• Record Type: Journal Article
• Authors: Sukums, Felix; Mzurikwao, Deogratias; Sabas, Deodatus; Chaula, Rebecca; Mbuke, Juliana; Kabika, Twaha; Kaswija, John; Ngowi, Bernard; Noll, Josef; Winkler, Andrea S.; Andersson, Sarah Wamala
• Is Part Of: Health policy and technology. Volume 12:Issue 1(2023)
• Publication Date: 2023-03
• Subjects: Artificial intelligence -- Machine learning -- Deep learning -- Neural network -- Health sector -- Tanzania; AI Artificial intelligence -- AJOL African Journal Online -- PRISMA-ScR Preferred Reporting Items for Systematic Review and Meta-Analysis Extensions for Scoping Review -- SSA sub-Saharan Africa -- ML Machine learning -- WHO World Health Organisation; Medical policy -- Periodicals; Medical technology -- Periodicals; Medical policy; Medical technology; Health Policy -- Periodicals; Biomedical Technology -- Periodicals; Technology Assessment, Biomedical -- Periodicals; Periodicals; 362.105
• DOI: 10.1016/j.hlpt.2023.100728
• Languages: English
• ISSNs: 2211-8837

Abstract

Highlights: 18 publications related to AI applications in the health sector in Tanzania. There are no national policies, regulations and guidelines for the adoption of AI. Rising interest in the adoption of these emerging technologies in health services. Technical, organisational, data and individual-related challenges hinder AI-driven innovations. Abstract: Background: Artificial Intelligence (AI) has great potential to transform health systems to improve the quality of healthcare services. However, AI is still new in Tanzania, and there is limited knowledge about the application of AI technology in the Tanzanian health sector. Objectives: This study aims to explore the current status, challenges, and opportunities for AI application in the health system in Tanzania. Methods: A scoping review was conducted using the Preferred Reporting Items for Systematic Review and Meta-Analysis Extensions for Scoping Review (PRISMA-ScR). We searched different electronic databases such as PubMed, Embase, African Journal Online, and Google Scholar. Results: Eighteen (18) studies met the inclusion criteria out of 2, 017 studies from different electronic databases and known AI-related project websites. Amongst AI-driven solutions, the studies mostly used machine learning (ML) and deep learning for various purposes, including prediction and diagnosis of diseases and vaccine stock optimisation. The most commonly used algorithms were conventional machine learning, including Random Forest and Neural network, Naive Bayes K-Nearest Neighbour and Logistic regression. Conclusions: This review shows that AI-based innovations may have a role in improving health service delivery, including early outbreak prediction and detection, disease diagnosis and treatment, and efficient management of healthcare resources in Tanzania. Our results indicate the need for developing national AI policies and regulatory frameworks for adopting responsible and ethical AI solutions in the health sector in accordance with the World Health Organisation (WHO) guidance on ethics and governance of AI for health.