Accelerating telehealth in South Africa: Bridging the gap between policy, AI innovation, and patient access
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Abstract
Background. The COVID‐19 pandemic accelerated global telehealth adoption, yet South Africa (SA) continues to face barriers owing to outdated regulations, fragmented infrastructure, and limited integration of artificial intelligence (AI). While national frameworks acknowledge the role of AI, a dedicated strategy for its implementation in healthcare is absent. This study draws on international governance frameworks and comparative policy analysis to explore pathways for inclusive and effective AI‐enabled telehealth in SA.
Objectives. To assess current telehealth and AI policy landscapes in SA, identify implementation gaps, and provide actionable recommendations for aligning national strategies with international standards and healthcare needs.
Methods. A qualitative policy analysis was conducted, reviewing SA regulatory instruments, including Health Professions Council of South Africa guidelines, the National AI Policy Framework and the National Health Insurance Act 20 of 2023, alongside the World Economic Forum (WEF)’s 2025 White Paper on AI in healthcare. A thematic comparative approach identified regulatory and infrastructural gaps, and proposed frameworks for sector‐specific policy advancement. No human participants were included.
Results. SA’s revised Telehealth Guidelines (2021) remain restrictive and lack provisions for AI integration. The National AI Policy Framework outlines ethical AI governance but fails to detail healthcare‐specific implementation. In contrast, the WEF framework offers a phased, stakeholder‐driven model for AI adoption, emphasising data governance, digital infrastructure, and workforce training – critical elements currently missing in SA’s approach.
Conclusion. A national AI‐in‐healthcare roadmap, supported by regulatory reform, a dedicated oversight body, and investment in digital infrastructure, is essential for equitable telehealth expansion. Further research should focus on scalable models for rural and underserved populations.
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