Shaping Public Health: Digital Health Innovations, Equity, and Accessibility in Africa

Adetayo Olorunlana () and Nnenna Okorie–Eugene ()
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Adetayo Olorunlana: Caleb University, Department of Public Health, College of Health Sciences
Nnenna Okorie–Eugene: Caleb University, Department of Nursing, College of Health Sciences

A chapter in Behavioral Cybersecurity and Ethical AI in Relational Economics Context, 2026, pp 51-81 from Springer

Abstract: Abstract Digital health innovations, particularly those leveraging artificial intelligence (AI) and machine learning (ML), represent a transformative shift in global healthcare. These technologies offer the potential to revolutionize healthcare delivery, improve medical research, and expand access to underserved populations in Africa communities. AI–powered tools, such as predictive algorithms, telemedicine platforms, and wearable health devices, enable the optimization of healthcare processes, early disease detection, and personalized treatment strategies. However, the integration of digital health technologies presents complex challenges, including ethical dilemmas, data privacy concerns, algorithmic bias, limited digital literacy, and gaps in regulatory frameworks. Addressing these challenges is critical to ensuring that these innovations contribute equitably to public health, sustainable strategies to resolve existing health disparities. This study employs a narrative review approach to synthesize diverse evidence on the role of digital health technologies in advancing equitable access to healthcare, with a particular focus on low–income countries in Africa. Literature was systematically sourced from academic databases such as PubMed, Scopus, and Google Scholar, as well as institutional reports from the World Health Organization (WHO) and the World Economic Forum. The study utilized Social Determinant Theory, Diffusion of Innovations Theory and the Health Equity Framework, offering theoretical insights into the adoption of digital health innovations and their impact on equitable health outcomes. The findings emphasize the need for inclusive and ethical practices in the design and implementation of AI and ML in healthcare. Inclusive data practices—such as diverse data collection, culturally sensitive AI development, and accessible AI solutions—are essential to overcoming algorithmic bias and ensuring equitable outcomes. Multidisciplinary collaboration from sociologist, healthcare professionals, data scientists, policymakers, and community advocates and residence is critical for developing solutions that align with local needs. Furthermore, community engagement, digital health literacy programs, and robust regulatory frameworks are vital to fostering trust in digital health technologies and promoting their adoption. This review highlights the importance of centering equity in digital health innovations to ensure that the benefits reach all population groups, especially marginalized and underserved communities. By prioritizing transparency, accountability, and inclusivity, digital health technologies can help bridge health disparities, improve population health outcomes, and build resilient healthcare ecosystems. The study concludes that AI and ML, when integrated responsibly and ethically, hold immense potential to transform global health systems, advance equitable care, and contribute meaningfully to sustainable healthcare solutions. These insights provide actionable recommendations for healthcare stakeholders to harness the transformative power of digital health innovations while addressing ethical, social, and policy barriers in Africa.

Keywords: Artificial Intelligence; Ethical AI; Healthcare Equity; Health Disparities; Health Technology Adoption; Machine Learning; Public Health Systems (search for similar items in EconPapers)
Date: 2026
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DOI: 10.1007/978-3-032-01214-2_3

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