Adoption of Artificial Intelligence to Support Healthcare and Clinical Education: A Scoping Review on Policymakers' and Users’ Perceptions in Nigeria and South Africa.

Abstract

Recent advancements in healthcare enhance personalised care and preventive medicine, and they also support advanced clinical trials. Healthcare technologies transform the traditional linear approach to a nonlinear one, enabling faster access to practice and informed decision-making. Artificial intelligence (AI) in healthcare integrates advanced technologies into healthcare systems to promote efficient, personalised medicine; precision interventions targeting specific diseases; enhanced healthcare accessibility; predictive analytics; and drug discovery. AI in healthcare has demonstrated significant benefits in reducing healthcare disparities and promoting equitable access to quality care. AI systems encompass machine learning, deep learning, natural language processing, and computer vision. AI systems process vast amounts of data to recognise patterns, understand and respond to language, make predictions, and interpret images using algorithms. This research aims to highlight the need for AI adoption in clinical practice and to elucidate its uptake among policymakers and users perceptions in both countries. My primary focus will be on Nigeria and South Africa, given their impact on healthcare in Africa. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA-ScR) protocol for scoping review is employed in a multi-approach data collection and analysis. Relevant scientific databases were searched for articles published from 2020 to 2025 on the following topics: “Artificial intelligence”, “Healthcare technologies”, “Healthcare policies & AI”, “Policymakers in Nigeria and South Africa”,” AI adoption” and “Health professionals ”. AI in clinical practice within healthcare frameworks will support clinicians in preventive interventions for early disease detection and treatment planning. It will educate them on integrating these AIs into personalised care without compromising ethics or privacy. Identified gaps include vague financial constraints, inadequate training, and the infrastructure required to overhaul the traditional method of personalised care. These gaps need to be addressed by future research so that healthcare policymakers can develop effective frameworks for governing Artificial Intelligence. Keywords: Artificial Intelligence, healthcare policymakers, Nigeria, South Africa, Healthcare policies and frameworks.