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Artificial intelligence for healthcare in Africa: a scientometric analysis

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Abstract

Introduction

Artificial intelligence (AI) has greatly transformed healthcare in developed countries. However, there is limited data describing the extent of AI adoption in African healthcare systems. The aim of this study was to understand the state of AI healthcare research in Africa.

Methods

A scientometric analysis was conducted to visualize the state-of-the-art research of AI in healthcare in Africa.

Results

Twenty-six relevant articles, published by 178 authors and affiliated with 96 organizations in 31 countries, were included. The most prolific African countries were South Africa, followed by Nigeria and Ghana. Some articles were published by authors affiliated with non-African countries. None of the contributing authors published more than 2 articles. Only 20 (11.2%) authors collaborated, forming a single cluster. The most common AI tools used in African health systems were deep learning neural networks applied in medical imaging, Adaptive Neuro-Fuzzy Inference Systems, and E-algorithms.

Conclusion

Our results suggest that AI for healthcare in Africa is still in its developmental phase with limited published research. Our social network analysis highlighted a South and West African predominance in the research relational network of AI in healthcare. This discrepancy presents an opportunity for coordination and increased collaboration with healthcare institutions advanced in the use of AI within Africa and beyond.

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Funding

Supported by NIH CTSA Grant Number TL1 TR001864 (B Njei), Yale Liver Center award NIH P30 DK034989 (B Njei).

Author information

Authors and Affiliations

  1. International Medicine Program, Section of Digestive Diseases, Yale University School of Medicine, 333 Cedar Street, New Haven, CT, 06510, USA

    Basile Njei

  2. Global Clinical Scholars Research Training Program, Harvard Medical School, Boston, MA, USA

    Basile Njei & Ulrick Sidney Kanmounye

  3. Research Department, Association of Future African Neurosurgeons, Yaoundé, Cameroon

    Ulrick Sidney Kanmounye

  4. Department of Medicine, Warren Alpert School of Brown University, Providence, RI, USA

    Mouhand F. Mohamed & Joao Filipe G. Monteiro

  5. Medical Physiology Program, Department of Physiology and Biophysics, Case Western Reserve University School of Medicine, Cleveland, OH, USA

    Anim Forjindam

  6. Internal Medicine Residency Program, Richmond University Medical Center, Staten Island, NY, USA

    Nkafu Bechem Ndemazie

  7. Department of Internal Medicine, Interfaith Medical Center, Brooklyn, NY, USA

    Adedeji Adenusi

  8. Federal Medical Centre, Yenagoa, Bayelsa State, Nigeria

    Stella-Maris C. Egboh

  9. Gastroenterology/Hepatology Unit, Department of Internal Medicine, University of Calabar, Calabar, Cross River State, Nigeria

    Evaristus S. Chukwudike

  10. Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, MA, USA

    Tyler M. Berzin

  11. Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA

    Akwi W. Asombang

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  1. Basile Njei
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Contributions

Data curation: Ulrick Sidney Kanmounye, Basile Njei. Formal analysis: Ulrick Sidney Kanmounye, Basile Njei. Funding acquisition: Basile Njei. Methodology: Ulrick Sidney Kanmounye, Basile Njei. Writing – original draft, review & editing: Basile Njei, Ulrick Sidney Kanmounye, Mouhand F. Mohamed, Nkafu Bechem Ndemazie, Anim Forjindam, Stella-Maris C. Egboh, and Adedeji Adenusi. Review of draft: All authors. Approval of the final draft for submission and publication: All authors.

Corresponding author

Correspondence to Basile Njei.

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Njei, B., Kanmounye, U.S., Mohamed, M.F. et al. Artificial intelligence for healthcare in Africa: a scientometric analysis. Health Technol. 13, 947–955 (2023). https://doi.org/10.1007/s12553-023-00786-8

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