AI in African Healthcare: Key Trends, Challenges, and Opportunities for Sustainable Innovation

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A recent study by Kondo et al. (2025) titled “Exploring the status of artificial intelligence for healthcare research in Africa: a bibliometric and thematic analysis” published in AI and Ethics, by Springer Nature, reveals that advancing AI in African healthcare requires locally driven research, robust infrastructure, ethical practices, and equitable global collaboration.

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Advancing AI in African healthcare requires local leadership, robust infrastructure, ethical practices, and equitable global collaboration.-Kondo et al. 2025

The study examines how Artificial Intelligence (AI) is being applied in healthcare research across Africa, offering a comprehensive overview of publication trends, key contributors, and emerging themes in this rapidly evolving field. Its primary goal is to quantify and analyze the volume of AI-related healthcare publications, identify leading authors, institutions, countries, and journals, and uncover the dominant technologies and themes shaping Africa’s AI healthcare landscape. It also highlights major challenges, gaps, and opportunities to guide future research and development.

Thematic Insights

The most frequently used keywords included machine learning, artificial intelligence, COVID-19, epidemiology, malaria, and tuberculosis. Emerging areas of focus such as digital health, deep learning, and global health point to the growing diversification of AI applications within healthcare. Foundational themes—AI, machine learning, Africa, and COVID-19—remain central yet continue to evolve, reflecting both growing interest and developmental potential.

Challenges and Gaps

Despite these advancements, the study identified several persistent challenges. A digital divide limits access to essential infrastructure in many regions, while a shortage of skilled AI specialists and healthcare professionals constrains innovation and application. Underrepresentation of African researchers and institutions in global AI healthcare literature remains a critical concern, as does the issue of ethical bias in AI models, often stemming from non-representative datasets that fail to capture Africa’s diverse populations.

How the Study was Conducted

The study used a bibliometric analysis to quantitatively examine academic literature on the application of Artificial Intelligence (AI) in healthcare across Africa. This approach helped identify research patterns, trends, collaborations, and thematic developments within the field.

Data were collected from Lens.org, with the search conducted on April 9, 2022, using the query:

(“Artificial Intelligence” OR “Machine Learning” OR “Deep Learning” OR “Natural Language Processing”) AND “Health*” AND “Africa”. Only English-language publications indexed in Lens.org and published up to April 9, 2022, were included, while non-English articles, those published after this date, and papers from other databases were excluded.

The bibliometric analysis followed a four-step process:

• Identification – Relevant literature was gathered using specific keywords related to AI and healthcare.
• Analysis – Publication metrics such as authorship, citations, and keyword co-occurrence were examined to understand scholarly impact and research focus.
• Presentation – The results were visualized using specialized bibliometric mapping tools.
• Contextualization – The findings were interpreted within the broader context of AI research and its healthcare applications in Africa.

For data processing and visualization, the researchers employed several tools. VOSviewer was used to map co-authorship networks and keyword co-occurrence patterns, while Bibliometrix generated thematic maps, topic trends, and three-field plots linking authors, keywords, and sources. Microsoft Excel was used for statistical summaries and the creation of visual charts showing publication trends, citation counts, and institutional productivity.

The analysis measured and visualized a range of bibliometric indicators, including annual publication outputs, types of publication outlets (such as journals and conferences), subject areas (e.g., computer science, medicine, biology), top journals and publishers, leading countries and institutions, as well as keyword trends and thematic clusters.

What the Authors Found

The analysis identified over 5,300 AI and healthcare-related publications, with a remarkable surge in output beginning after the year 2000. Journals accounted for about 80% of all publications, followed by conference papers, book chapters, and technical reports, confirming that scholarly journals remain the main communication channel for disseminating AI healthcare research.

The research revealed that the most active subject areas were Computer Science (1,070 publications), Medicine (881), and Biology (524), with Psychology, Public Health, and Business also contributing significantly. Among the leading journals, the Annals of Tropical Medicine and Public Health topped the list, while Elsevier emerged as the most prolific global publisher in this domain.

In terms of geographical distribution, South Africa, Nigeria, and Kenya were the leading African contributors, while globally, the UK, USA, and South Africa dominated the field. Harvard University was identified as the most active institution worldwide, producing 143 publications, and notably, the University of Cape Town was the only African university to appear in the global top ten.

Why is this important

Spotlighting Africa’s Role in Global AI Healthcare: The study highlights Africa’s position in the global AI healthcare landscape, showcasing contributions from countries like South Africa, Nigeria, and Kenya. It also reveals under-representation from other regions, calling for more inclusive participation across the continent.

Guiding Strategic Investment and Policy: By mapping research trends, institutions, and funding sources, the study provides a roadmap for governments and organizations to strengthen AI infrastructure. It offers evidence-based insights for policymakers to support AI education, research, ethical standards, and targeted funding toward neglected diseases and technologies.

Accelerating Context-Specific Innovation: Recognising Africa’s unique healthcare challenges—such as infectious diseases, limited medical resources, and specialist shortages—the study encourages the development of AI tools tailored to local contexts. It promotes research on diseases like malaria and tuberculosis and advocates for affordable, scalable AI solutions for underserved communities.

Empowering Local Institutions and Researchers: The findings show that most top institutions and leading authors in AI healthcare are based outside Africa. This underscores the need for capacity building within African universities and research centers, greater international collaboration, and support for emerging African researchers to drive innovation from within the continent.

Addressing Ethical and Equity Concerns: The study emphasizes that AI systems must be trained on diverse, representative datasets to avoid bias. It calls for ethical frameworks and inclusive data practices that ensure fair access, transparency, and equitable benefits for all regions.

What the Authors Recommended

• Despite rapid global progress, Africa still lags in AI adoption within clinical care. Governments and health systems must design targeted strategies that address local challenges such as limited infrastructure, data scarcity, and workforce shortages, ensuring AI solutions are practical, scalable, and contextually relevant.
• Empower African universities and research institutions to lead in AI-driven healthcare innovation. Support the development of region-specific models aligned with Africa’s disease burden—such as malaria, tuberculosis, and neglected tropical diseases—and ensure their findings are high-quality and citable to boost global visibility.
• Foster equitable partnerships between African institutions and global leaders (e.g., Harvard, Oxford) to enable knowledge exchange, joint research, and capacity building. Encourage cross-border projects that position African scholars as equal contributors in the global AI health research ecosystem.
• Invest in robust electronic health record systems, internet connectivity, and data governance frameworks. Strengthen healthcare data collection, storage, and interoperability to create reliable datasets for training and deploying AI models effectively across the continent.
• Promote bias mitigation by using diverse datasets representing African populations. Frame AI as a driver of health equity and social justice, not just efficiency. Secure long-term funding through local and global partnerships to sustain research, implementation, and bibliometric monitoring for continuous improvement.

In conclusion, the study by Kondo et al. (2025) offers a vital roadmap for advancing AI-driven healthcare across Africa. By uncovering publication trends, highlighting key contributors, and addressing pressing challenges such as data gaps, limited infrastructure, and ethical bias, the research underscores the urgent need for inclusive, locally led innovation. Strengthening institutional capacity, fostering equitable global partnerships, and investing in robust data ecosystems will be essential to unlocking AI’s full potential for improving health outcomes across the continent. Ultimately, the study calls for a future where AI in healthcare not only advances technology but also promotes equity, resilience, and sustainable development in Africa.