Mapping the regulatory landscape of AI in healthcare in Africa

Front Pharmacol. 2023 Aug 24:14:1214422. doi: 10.3389/fphar.2023.1214422. eCollection 2023.

Abstract

Introduction: Artificial intelligence (AI)-enhanced technology has seen unprecedented expansion in the recent past. This growth brings with it huge opportunities for the positive transformation of the economy, business, healthcare, and society. However, a critical question is whether, and to what extent, regulatory measures and mechanisms have been implemented to safeguard its design, development, and deployment. This paper offers a scoping exercise that maps the regulatory landscape of AI in healthcare (including health research) in certain African countries. Methods: This research is conducted across 12 African countries: Botswana, Cameroon, The Gambia, Ghana, Kenya, Malawi, Nigeria, Rwanda, South Africa, Tanzania, Uganda, and Zimbabwe. As limited specific AI legislation is found in these African countries, and because AI is informed by ancillary regulatory frameworks, we include data protection, digital health, consumer protection, and intellectual property in our research. A scoping review method was applied with a manual search of digital libraries with search terms customised for each repository consisting of core search terms for the various topics, including, among others, "law," "regulation," "artificial intelligence," "data protection," "intellectual property," and "digital health". Results and discussion: Analysis of the data demonstrated that while in the African countries under investigation there is no sui generis AI regulation, recent developments were found in areas that inform AI adoption, including in digital health, data protection, consumer protection, and intellectual property. Our findings highlight the fragmentation of the African AI regulatory landscape and illustrate the importance of continued AI regulatory development to ensure that Africa is well positioned for future AI adoption in health.

Keywords: AI; Africa; artificial intelligence; healthcare; landscape; regulation.

Publication types

  • Systematic Review

Grants and funding

We acknowledge the support by the US National Institute of Mental Health and the US National Institutes of Health (award number U01MH127690). The work of BT was funded by the UKRI project EP/V026747/1 “Trustworthy Autonomous Systems Node in Resilience.” The content of this article is solely our responsibility and does not necessarily represent the official views of the US National Institute of Mental Health or the US National Institutes of Health.