Google Research

Machine learning for healthcare: A bibliometric study of contributions from Africa

  • Houcemeddine Turki
  • Anastassios Pouris
  • Francis-Alfred
  • Michaelangelo Ifeanyichukwu
  • Catherine Namayega
  • Mohamed Ali Hadj Taieb
  • Sadiq Adewale Adedayo
  • Chris Fourie
  • Christopher Brian Currin
  • Mercy Nyamewaa Asiedu
  • Atnafu Lambebo Tonja
  • Abraham Toluwase Owodunni
  • Abdulhameed Dere
  • Chris Chinenye Emezue
  • Shamsudden Hassan Muhammad
  • Muhammad Musa Isa
  • Mohamed Ben Aouicha
Preprints (2023)


Machine learning has seen enormous growth in the last decade, with healthcare being a prime application for advanced diagnostics and improved patient care. The application of machine learning for healthcare is particularly pertinent in Africa, where many countries are resource-scarce. However, it is unclear how much research on this topic is arising from African institutes themselves, which is a crucial aspect for applications of machine learning to unique contexts and challenges on the continent. Here, we conduct a bibliometric study of African contributions to research publications related to machine learning for healthcare, as indexed in Scopus, between 1993 and 2022. We identified 3,772 research outputs, with most of these published since 2020.

North African countries currently lead the way with 64.5% of publications for the reported period, yet Sub-Saharan Africa is rapidly increasing its output. We found that international support in the form of funding and collaborations is correlated with research output generally for the continent, with local support garnering less attention. Understanding African research contributions to machine learning for healthcare is a crucial first step in surveying the broader academic landscape, forming stronger research communities, and providing advanced and contextually aware biomedical access to Africa.

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