Jul 2026· Interdisciplinary Journal of the African Alliance for Research, Advocacy and Innovation· Vol 2· 0 citations· 2 references
TL;DR
This commentary argues that the future of AI in African healthcare should be guided by local leadership, interdisciplinary collaboration, and equitable partnerships, and calls for governments, universities, researchers, healthcare institutions, and the private sector to work together to build an innovation ecosystem in which artificial intelligence strengthens health systems while advancing scientific independence.
Abstract
Artificial intelligence is rapidly changing the way health systems deliver care, generate knowledge, and support decision making. Across the world, AI is being used to strengthen disease surveillance, improve diagnostic accuracy, accelerate drug discovery, and expand access to healthcare through digital platforms. These developments present important opportunities for Africa, where persistent shortages of healthcare workers, growing disease burdens, and unequal access to specialist services continue to challenge health systems.
At the same time, the benefits of AI will not be realised automatically. Without deliberate investment in digital infrastructure, local research capacity, data governance, ethical regulation, and workforce development, there is a real risk that Africa will remain primarily a consumer of technologies designed elsewhere. Such an outcome could deepen existing inequalities and limit the continent's ability to shape technologies that reflect its own health priorities and cultural contexts.
This commentary argues that the future of AI in African healthcare should be guided by local leadership, interdisciplinary collaboration, and equitable partnerships. It calls for governments, universities, researchers, healthcare institutions, and the private sector to work together to build an innovation ecosystem in which artificial intelligence strengthens health systems while advancing scientific independence. Africa's role in the AI era should extend beyond technology adoption to active leadership in developing solutions that contribute to both regional and global health
Artificial intelligence (AI) is widely regarded as one of the most promising innovations in healthcare, yet its adoption in routine clinical practice remains limited. Only a small proportion of AI applications developed in research settings are successfully integrated into healthcare delivery. Major barriers include poor interoperability with existing health information systems, complex regulatory requirements, limited scientific evidence, and the lack of clear clinical guidelines. Many AI tools have been evaluated through methodologically weak studies, often retrospective and lacking external validation, contributing to skepticism among healthcare professionals. Additional challenges involve healthcare professionals' education and training, algorithm transparency, and the ability of healthcare organizations to effectively incorporate these technologies into clinical workflows. To promote the safe and effective adoption of AI, stronger clinical evidence, structured training programs, and organizational models capable of supporting its implementation are required. Addressing these issues is essential to ensure that AI can deliver meaningful benefits for patients, healthcare professionals, and healthcare systems.
Eugenio Santoro· Recenti progressi in medicin...· 0 citations
Artificial Intelligence (AI) has become an emerging field with the potential to revolutionize the healthcare sector. Despite healthcare digitization, the problem of healthcare access inequalities still exists. For instance, in developing countries, there is often an insufficient number of skilled professionals and healthcare facilities, resulting in inadequate diagnosis, treatment, and healthcare access. Thus, the current paper examines the role of artificial intelligence in healthcare equality of the underserved population in Asia and Africa, based on a scoping review of AI-led healthcare studies and reports. The review aims to explore the possibilities of AI in transforming healthcare for the better in low-and middle-income countries and focus on how these solutions can be implemented in the context of social justice and health equity. The review follows the PRISMA-ScR guideline and includes the application of AI in diagnosis, telehealth, disease prediction and surveillance, patient monitoring, and healthcare management. The results of the research indicate that the implementation of AI in healthcare can make it more accessible and equitable by ensuring the early detection of diseases, providing access to remote healthcare, and optimizing treatment and management in both urban and low-resource settings. Nevertheless, there are several barriers to the adoption of AI in healthcare, including the lack of digital infrastructure and healthcare data and the ethical, regulatory, and technical challenges associated with the technology. Overall, the research identifies the key aspects of AI application in health care and emphasizes their importance in promoting healthcare equity in developing countries. The research contributes to the existing literature by providing a conceptual basis for addressing health disparities in low and middle-income countries (LMICs) through the lens of digital technologies. The study recommends that future interventions prioritize equitable, affordable, sustainable, and ethical healthcare through the implementation of AI technologies, algorithms, and programs.
Artificial intelligence (AI) is increasingly promoted as a tool to enhance clinical decision-making and thus improve the quality of healthcare. While much of the emerging scholarship on AI and healthcare in Africa has focused broadly on opportunities and systemic challenges, what remains underexplored is the specific application of AI to clinical decision-making. This paper contributes to addressing this gap by offering a conceptual and critical analysis of AI-based clinical decision support systems (AI-CDSS) in African contexts. Drawing on philosophical accounts of medical reasoning and relational moral frameworks such as Ubuntu, the paper draws on the moral ecology of care and shows that algorithmic systems can reconfigure epistemic authority, redistribute responsibility, and risk marginalising context-sensitive and relational dimensions of care. The paper further argues that AI systems are better understood as socio-technical mirrors that reflect and amplify existing human values, institutional arrangements, and power asymmetries. Moving beyond the algorithm, it proposes a shift toward relational and context-sensitive AI governance, including the development of relational impact assessments, the redistribution of responsibility across the AI lifecycle, and the co-production of knowledge with local stakeholders. While focusing on African clinical contexts, the analysis offers broader insights for global debates on AI ethics and clinical decision-making.
K. M. Mussie· Science and Engineering Ethi...· 0 citations
ABSTRACT Background Artificial intelligence (AI) is transforming global health care through innovations in deep learning, generative models and agentic AI systems. Traditional reductionist approaches to complex pathophysiological pathways fail to capture the true complexity of disease, motivating the adoption of network medicine (NM), which models biological systems as dynamic, interconnected networks. When combined with AI, NM enables integration of multiomic data and better characterizes disease mechanisms to guide precision therapies. Nevertheless, the rapid diffusion of AI in health care also raises profound ethical, regulatory and social challenges, since only a small fraction of AI tools have achieved routine clinical use, often due to limited generalizability, opaque algorithms and workflow incompatibility. Methods Key strategies in this scenario are explainable AI (XAI) and counterfactual reasoning, which enhance transparency, accountability and fairness. These methods allow clinicians and regulators to interpret model decisions, identify biases and ensure human oversight in high‐stakes contexts. Ethical frameworks such as the European Commission's Assessment List for Trustworthy AI (ALTAI) operationalize these goals through auditable requirements spanning transparency, fairness, privacy and societal well‐being. Results Yet, challenges persist, including algorithmic bias, data inequity and variable regulatory standards across regions. Machine learning and generative AI hold significant promise for improving diagnostics, drug discovery and population health, but their deployment must be guided by fairness, transparency and human rights principles. Indeed, inadequate governance risks can impact the already existing health inequalities. Conclusions For these reasons, the convergence of AI, NM and telemedicine requires a co‐evolutionary model which must be rooted in ethical design, rigorous validation and equitable global implementation. Only by aligning technical innovation with sound ethical frameworks and explainability standards will AI become a highly transformative yet trustworthy force in the field of precision and public health.
P. Portincasa, M. Khalil, P. Novielli et al.· European Journal of Clinical...· 0 citations
Artificial Intelligence (AI) is progressing at a pace that often exceeds how quickly educational, healthcare, public health, and policy systems can keep up with it, let alone govern, evaluate, and deploy it responsibly. In healthcare, public health, and education, it is broadly discussed as a tool that could benefit doctors, researchers, students, and decision-makers. But those benefits will not happen by themselves. If AI is shaped mostly by groups that already have the resources such as data, computing power, funding, technical skills, and policy influence, it will widen the same gaps it is supposed to close.
For MIT Science Policy Review Volume VII, themed "The Geopolitics of Science Policy: How Science is Outpacing Policy in a Rapidly Changing World," MIT SPR spoke with three experts whose work spans global policy, academia, public health, and technology implementation around one direct question: "Is AI a scientific revolution or a new digital divide?"
P. Podila, Chandra S. Inguva· MIT Science Policy Review· 1 citation
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