Jul 2026· Journal of medicine and health research· Vol 11, pp. 139-151· 0 citations
TL;DR
The promise of AI for LMIC health systems needs to be supported by robust, place-sensitive governance structures that extend beyond technology alone.
Abstract
Background: Artificial intelligence (AI) has the potential to transform healthcare delivery in low- and middle-income countries (LMICs), where the disease burden from preventable causes is high and healthcare resources are severely limited. Although major technological breakthroughs have occurred, systemic, regulatory, and infrastructural barriers continue to hinder the implementation of AI-powered health solutions in these settings.
Objectives: This review aims to examine and synthesise key data governance (DG) challenges and concerns in the context of health systems in LMICs, develop an analytical framework for assessing readiness to address data governance issues in AI-enabled health systems, and identify key areas for data governance policy interventions and future research.
Methods: A structured narrative review of peer-reviewed literature from PubMed, Scopus, and Web of Science, together with relevant grey literature sources from January 2015 to December 2023, was conducted. AI governance, digital health infrastructure, digital health data regulation, and algorithmic accountability in LMICs were considered. Findings were analysed thematically to synthesise governance concerns across key governance dimensions.
Results: Seven major governance challenge domains were identified: (1) weak and fragmented regulatory frameworks; (2) poor data quality and limited interoperability; (3) inadequate patient data protection mechanisms; (4) algorithmic bias due to undersampling of local populations; (5) inadequate digital health infrastructure; (6) limited digital literacy among healthcare professionals; and (7) ethical tensions related to consent, privacy, and community trust. Cross-cutting themes of power asymmetry in global AI development and donor dependency in digital health financing were also identified.
Conclusions: The promise of AI for LMIC health systems needs to be supported by robust, place-sensitive governance structures that extend beyond technology alone. Before equitable AI adoption can be pursued, investments in regulatory capacity, data systems, algorithmic accountability mechanisms, and stakeholder engagement are critical preconditions. A Governance Readiness Framework is proposed to support policymakers, health ministries, and development partners.
This paper reconceptualises Artificial Intelligence (AI) as a governance infrastructure within African health systems. Many health systems across Africa face structural constraints, including workforce shortages, fragmented data systems, and limited infrastructure, which undermine service delivery and equity. The paper argues that AI enables a transition from reactive, fragmented systems to predictive, integrated, and data-driven governance models. It examines key applications in diagnostics, disease surveillance, supply chain management, and telemedicine, highlighting how AI improves decision-making, resource allocation, and access to care. At the same time, the study identifies critical challenges, including data privacy risks, algorithmic bias, and technological dependency. The findings suggest that the transformative potential of AI depends on strong governance frameworks, institutional capacity, and locally grounded policies. By positioning AI as a governance infrastructure, the paper contributes to understanding how digital innovation can strengthen resilience, efficiency, and equity in African health systems.
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 used to support healthcare governance by improving planning, resource allocation, policy development, and organizational decision-making. However, existing approaches to AI evaluation remain largely focused on technical performance and clinical effectiveness, providing limited guidance for assessing AI systems used in healthcare management and public administration. This study aims to develop a multidimensional framework for evaluating the effectiveness of AI in healthcare governance. The study is based on a narrative review and synthesis of contemporary scientific literature, international guidelines, health technology assessment frameworks, and AI governance recommendations. The proposed framework integrates six complementary evaluation dimensions: technical, clinical, organizational, economic, governance, and ethical. For each dimension, key evaluation objectives and recommended indicators are identified based on evidence from international methodological frameworks and recent research. The framework is intended to support healthcare managers, policymakers, researchers, and healthcare organizations in conducting comprehensive assessments of AI systems throughout their lifecycle. Unlike existing approaches that typically evaluate individual aspects of AI implementation, the proposed framework combines multiple evaluation perspectives into a unified model specifically tailored to healthcare governance. The findings contribute to the development of standardized approaches for evaluating AI-enabled decision support systems and may facilitate more evidence-informed adoption of AI technologies in healthcare management.
Yurii Cherleniuk· Scientific Journal of Poloni...· 0 citations
This study systematically analyzes research trends in health AI over the past six years through a systematic literature review (SLR) and a bibliometric analysis using VOSviewer to highlight dominant research areas, including machine learning for diagnosis, AI-driven hospital management, and predictive analytics.
Irwan Bastian, Aqilla Rahman Musyaffa, Lukman Nulhakim et al.· IAES International Journal o...· 0 citations
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
E. Elliason· Interdisciplinary Journal of...· 0 citations
Healthcare technologies are increasingly reshaping how diseases are detected, monitored, treated, and managed across healthcare systems. Advances in artificial intelligence (AI), digital health, remote monitoring, advanced medical devices, and data-driven clinical infrastructures are creating important opportunities to improve prevention, diagnostic accuracy, personalisation of care, workflow efficiency, and long-term healthcare sustainability within the framework of predictive, preventive, personalized, and participatory medicine (4P Medicine). However, despite growing technological sophistication and investment, many innovations fail to achieve scalable and sustainable implementation in real-world clinical environments. This narrative review critically examines the systemic factors that condition the successful translation of healthcare technologies into routine clinical practice, with particular emphasis on the European and Spanish contexts. Rather than focusing exclusively on technological performance, the review analyses the broader regulatory, organisational, financial, ethical, and governance challenges that shape implementation. Key areas discussed include technology transfer, regulatory frameworks, health data governance, and the organisational challenges associated with implementing AI-driven healthcare technologies. The central argument of this review is that the real-world impact of healthcare innovation depends less on technological capability itself than on the capacity of healthcare systems to support validation, regulation, implementation, workforce adaptation, interoperability, and long-term governance. Consequently, the principal challenge for contemporary healthcare systems is no longer simply how to develop new technologies, but how to integrate them safely, equitably, and sustainably into routine clinical practice.
María Vallet-Regí, M. Doblaré, J. A. Garrido et al.· Frontiers in Digital Health· 1 citation
