{"paper_id":"f605afce-d376-40ec-a953-cbb45a10fec5","body_text":"Revolutionizing African Healthcare: A Systematic Review of Artificial Intelligence and Data Governance | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Systematic Review Revolutionizing African Healthcare: A Systematic Review of Artificial Intelligence and Data Governance Bitange Ndemo This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6572611/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background: African health care Systems are confronted by continuing infrastructure disparities or lack of capacity about the workforce and unequal access to care. To address these challenges, artificial intelligence (AI) and data governance frameworks have become the change makers in the health sector, especially in the wake of the African Union Convention on Cyber Security and Personal Data Protection (2014) which was used to provide the foundation upon which digital innovators look to bring solutions to life in the healthcare sector. Purpose: This study investigates the integration of AI in African healthcare systems post-2014. It examines the role of AI in disease detection, patient management and other health outcome, ethical standards and policy development implications. Methods: A total of 41 peer-reviewed articles published in the years between 2015 and 2023 were analysed by means of a systematic review of the literature. PubMed, IEEE Xplore, and Google Scholar were used as the sources. Search terms included “AI in healthcare,” “data governance” and “Africa,”. Also, the review has included the grey literature which did not follow the PRISMA guidelines. Empirical evidence of implementation of AI across African countries in the healthcare contexts was the basis of the studies selected. Results: The number of publications related to healthcare via AI has increased by 35% in 2022-2023 colocation with increasing interest and investment in this issue. It was found out that AI: contributes to significant parts of diagnostic accuracy, operational efficiency and predictive analytics in several medical disciplines. Despite these critical challenges, there remain two major sticking points regarding infrastructure, regulatory enforcement, and usage of patient data in the ethical way. Conclusions: The study indicates the need for the immediate development of targeted interventions by the African governments, health ministries, research institutions, and digital health stakeholders. With these include the establishment of robust governance mechanism, ethical oversight frameworks and digital infrastructure as investment. In the future, it is suggested that longitudinal study and cross regional policy comparison should be conducted to provide sufficient support for sustainability of AI integration in healthcare systems. Agricultural Economics & Policy Artificial Intelligence Data Governance Healthcare Systems Ethics Health Policy Africa Artificial Intelligence Healthcare Ethics Data Governance Health Policy Africa Figures Figure 1 Figure 2 Figure 3 Figure 4 1. Background Since the mid-1980s, artificial intelligence (AI) was integrated into the broad healthcare context and initially piloted in Kenya as Medical AI (MAI) (Owoyemi et al., 2020 ). This period was marked by the development of expert systems, which utilized predefined rules and logical principles to mimic human knowledge in specific medical domains. The systems used data inputs to generate diagnostic suggestions and developed into modern healthcare AI applications (Seto et al., 2012 ). Medical practitioners focused on extracting knowledge from unstructured medical texts through the entire span from early 2000s until the 2010s. Natural language processing systems together with text-search methods enabled medical and research records as well as health papers to be extensively investigated to extract detailed information. The advancement made it possible for medical professionals to produce specific useful knowledge from large amounts of medical texts for better healthcare decisions (Dreisbach et al., 2019 ). AI methods underwent substantial developments in medical image interpretation following the year 2010. The technology of convolutional neural networks achieved top performance in photo processing functions which included picture element identification along with segmentation and classification projects. The recent technological progress enabled medical practitioners in Africa to receive enhanced comprehension of diagnostic images including X-rays and MRIs and CT scans. The improved capability to identify patients alongside enhanced anomaly detection was made possible by this aspect (Gao et al., 2018 ). Currently, AI applications in the healthcare sector include disease prediction, in-depth understanding and the operation of robots that assist in surgical procedures (Zuhair et al., 2024 ). These applications transformed AI into a potent force, which revolutionized modern healthcare practices. Notable examples of AI utilization in Africa highlight its impact on improving human life and fostering openness, safety and operational ease. The effectiveness of AI in healthcare is heavily reliant on access to suitable data sources, particularly big data, to execute tasks efficiently and expeditiously (Kaplan & Haenlein, 2020 ). AI and data governance frameworks are being introduced into African healthcare systems as a response to structural and systemic healthcare challenges is not prima facie evidence in favour of the technological determinist stereotype. Healthcare systems in the entire continent are not homogeneous in terms of infrastructure, human resources, institutional capacity and technological readiness. Rural and urban health care, clinical and nonclinical systems, and sub disciplines whose members provide health care such as pharmacy, nursing, radiology and clinical laboratory sciences are found to have pronounced disparities. Each sub discipline has its own classification system and coding structure. IMO Health, a leading medical data organization, identifies, four most widely used standardized coding systems including International Classification of Diseases ICD-11, Current Procedural Terminology (CPT), Systemized Nomenclature of Medicine (SNOMED) and Logical Observation Identifiers Names and Codes (LOINC) as standardized procedural codes in different disciplines. Other standards like Terminologia Anatomica (TA) are used as a global standard for anatomical terminology (Chamberlain, 2024 ). The codes are a reminder of how the data exists across institutions and regions, which is some complexity. The lack harmonization and semantic interoperability may lead to a poor performance of AI systems or reinforcement of existing disparities. African health systems have developed in peculiar socio political and legal contexts. For example, South Africa’s health care system moderns itself in the wake of apartheid era institutional segregation by persistent bifurcated service delivery architectures that persistently defy disintegration. History dictates the structure, priorities and effectiveness of the healthcare systems’ response to new technologies such as AI. Based on typological frameworks, healthcare delivery models also are characterized in varying degrees of entrepreneurial (market driven) from welfare oriented and universal systems. Some systems are centralized, others decentralized; some publicly funded, others dominated by private actors. These differences impact how AI and data governance mechanisms are implemented, scaled, and regulated. To better integrate AI, it's also useful to consider the healthcare infrastructure in layered terms: (1) the business layer (e.g., care delivery workflows), (2) the data layer (e.g., patient records, test results), (3) the application layer (e.g., clinical decision support systems), and (4) the technology layer (e.g., networks, cloud services). Each layer presents unique challenges and must be addressed in governance frameworks and AI implementations. Thus, a one-size-fits-all approach to AI in African healthcare is inadequate. Interventions must be context-specific and cognizant of layered healthcare architecture, sector-specific norms, historical inequities, and typological diversity. These insights establish the rationale for evaluating how AI and data governance have been approached across the continent post-2014.The increased demand for data to train various health models led to concerns about data security and privacy. As a result, the African Union (AU) adopted the AU Convention on Cyber Security and Personal Data Protection (AUCCPDT) in 2014. Several countries participated in the development of national data governance policies (Michel et al, 2020) and by early 2023, at least 27 countries passed laws, and eight others were in the process (Ndemo & Mkalama, 2024). Post-2020 witnessed increased dependence on digital health records and extensive data repositories in predicting medical outcomes and providing personalized treatment in Africa. Machine learning and data mining enable the analysis of vast datasets (Marques et al, 2019), which helped to reveal the trajectory of illnesses, assess associated risks and modify treatment strategies based on individual patient requirements (Hulsen et al., 2019 ). Table 1 presents the chronological Framework on AI and Healthcare in Africa. 1.1. Contextual Challenges of AI and Healthcare in Africa AI adoption in Africa continues to expand but limited implementation occurs because the region lacks technological equipment along with inadequate healthcare systems combined with a lack of digital literacy skills. Throughout Africa there are inconsistent levels of technology access which create barriers to its utilisation. The necessary infrastructure needed to operate AI programmes does not exist in various rural and underserved regions (Ciecierski-Holmes et al., 2022 ; Staunton et al., 2023 ). Software deployment in healthcare struggles primarily because medical institutions have difficulties implementing proper hardware systems and fail to maintain high-speed networks and stable connexions. The effectiveness of healthcare professionals is restricted by their different levels of digital literacy skills. The proper implementation of AI demands continuous learning programmes which can assist professionals to achieve maximum potential from their technology-driven solutions. Different stages of healthcare infrastructure development across regions act as substantial obstacles to programme implementation. Adding AI technologies proves impossible because healthcare facilities lacking resources are unable to implement these systems. The absence of both financial funding and technical experts keeps these facilities from adopting new technological solutions (Owoyemi et al., 2020 ). AI application becomes highly restricted in places which badly need its benefits as they lack the resources to deploy this technology. The mentioned difficulties in implementation did not stop professionals from attempting to surmount them. It is essential to boost digital infrastructure while offering specific healthcare training to staff and to ensure resource distribution remains fair (Adebamowo et al., 2023 ). Healthcare AI requires support from coordinated actions between national governments and private stakeholders and international organisations to develop environments that can optimise its contributions to patient wellness. The effectiveness of AI in healthcare depends completely on getting suitable data sources mainly massive data so the system can work fast and efficiently (Kaplan & Haenlein, 2020 ). Rising digital record management practises across Africa lead to greater need for AI systems to forecast medical outcomes and deliver individualised treatments. Procedures using data mining and machine learning techniques study vast datasets to show medical illness patterns and evaluate risk factors simultaneously using individual patient preferences to generate modified treatment plans (Hulsen et al., 2019 ). The successful operation of AI algorithms relies on ready reliable data. African countries face major difficulties accessing structured and correct comprehensive datasets because of missing records and data entry mistakes together with irregular data collection methods (Tanui et al., 2024 ). The reliability of predictable models develops from high-quality data but inadequate data leads to poor results that impact clinical decisions and patient outcomes negatively. Table 2 Summary of Existing Reviews on AI and Data Governance in African Healthcare Study Type of Review Scope Identified Gaps Owoyemi et al. ( 2020 ) Narrative Review General applications of AI in African healthcare Lacks systematic methodology and focus on data governance Ciecierski-Holmes et al. ( 2022 ) Systematic Scoping Review AI in LMICs with African representation Limited to general systems strengthening; does not focus on post-2014 data governance landscape Adebamowo et al. ( 2023 ) Expert Commentary Data science potential in African health research Theoretical scope, no structured evidence synthesis Alami et al. ( 2020 ) Global Systematic Review AI innovation in LMICs Does not highlight African-specific governance and implementation challenges Gwagwa et al. ( 2021 ) Landscape Report Responsible AI in Sub-Saharan Africa Does not use systematic review methodology, lacks healthcare-specific detail This review builds on these studies by systematically analyzing peer-reviewed literature from 2015 to 2023, focusing explicitly on the intersection of AI, healthcare, and data governance in Africa post-AUCCPDT (2014). It addresses the lack of structured comparative analysis and contextual insights into regulatory frameworks, ethics, and implementation outcomes. 1.2. Objectives This research evaluates the evolution and impacts of AI and data governance frameworks in Africa since 2014, focusing on their contribution to improved healthcare delivery, patient outcomes, and data management. This study aims to create a comprehensive evidence map analyzing AI applications and data governance strategies in Africa’s healthcare interventions, focusing on operational outcomes, challenges, and lessons. 2. Methods The research investigates the systematic applications regarding the ties between AI and healthcare data protection throughout African nations. The approach validates results through evaluations of health practise elements as well as regulatory standards and technological advancement measures (Gerke et al., 2020 ). The comprehensive review discovers missing information which enables policymaking teams along with practitioners to gain strategic perspectives to establish a solid evidence foundation. The authors used the PRISMA 2020 protocol which serves as a standardised framework for systematic review to guarantee both reliability and robustness. Through this method researchers reduce bias while allowing others to repeat their work and enhance the research credibility in the field thereby demonstrating comprehensive quality. A PRISMA flow chart describing the article selection is shown in Fig. 1 (and summarised) in Fig. 2 .After exclusion of duplicates, 41 articles overall were included for final analysis. The databases consisted of chosen combinations of the databases that include IEEE Xplore (15), Google Scholar (58), ACM Digital Library (12), PubMed (35), and Other Sources (7). An initial set of n = 35 was after duplicate removal (n = 35), screening, and eligibility checks, and 41 of 35 (n = 41) peer reviewed empirical articles were included after review including grey literatures due to the need to keep the methodological rigour of the review. Moreover, all typographical problems were solved in the updated PRISMA diagram: The study utilized a well-organized methodology to explore the convergence among AI, data control, and healthcare components in Africa. It utilized leading academic databases like IEEE Xplore, Google Scholar, ACM Digital Library, and PubMed to ensure access to original, up-to-date articles. A comprehensive research strategy was established (Table 2 ). The rigorous selection process ensured relevance and significance of selected articles, providing a comprehensive, reliable, and interdisciplinary approach to literature evaluation. These databases provide a solid platform for investigating convergence in Africa. The research plan was designed to search these databases using Boolean operators (“AND,” “OR” and “NOT”) and specific keywords. This approach sought to encompass a wide range of literature in which AI, data control and healthcare intersect, to incorporate extensive and focused research findings. The search plan strategically aimed to identify articles that covers different aspects of the topic and diverse perspectives and insights into the complex dynamics among the three components in Africa. Table 3 Research Strategy Database Keywords Inclusion Criteria Exclusion Criteria IEEE Xplore “AI in healthcare,” “data governance,” “Africa” Peer-reviewed journal articles; empirical studies; English language; 2015–2023 Editorials, reviews, non-English texts PubMed “AI AND healthcare AND Africa” Peer-reviewed articles; empirical evidence; studies conducted in African contexts; English; 2015–2023 Studies without direct healthcare relevance; conceptual papers only Google Scholar “Artificial intelligence in African healthcare” Peer-reviewed; full-text available; empirical data; 2015–2023 Non-peer-reviewed, blogs, dissertations, grey literature ACM Digital Library “Data governance in African health systems” Peer-reviewed; empirical studies; African region focus; English; 2015–2023 Non-African focus; theoretical or policy-only articles Other Sources Hand searches, cited references Peer-reviewed African-focused studies aligned with AI/data governance Grey literature, conference posters All sources shared the same core criteria for inclusion: empirical, peer-reviewed, African context, and English language. The decision to start from 2015 stems from the 2014 African Union Convention on Cyber Security and Personal Data Protection, which marked a significant policy shift in data governance on the continent—making 2015 a logical start point for post-convention impact assessment. 2.1. Data Extraction A standardized data extraction form was developed and piloted to capture author(s), year, country, study design, AI application domain, data governance strategy, health system layer addressed (business/data/application/technology), outcomes, and ethical/legal considerations. Two reviewers independently extracted data from each article to ensure consistency and accuracy, with disagreements resolved through discussion. The process was grounded in ethical principles for secondary data use and emphasized responsible handling of potentially sensitive information (Zuiderwijk et al., 2021 ). Table 4 summarises included studies by year. Table 4 Number of Studies Used Year Number of Studies 2018 5 2019 11 2020 10 2021 8 2022 17 2023 18 2.2. Screening and Analysis Screening followed a two-stage approach: 1) Title and abstract screening for relevance to the three core pillars: AI, healthcare, and data governance in Africa. 2) Full-text screening for empirical depth, geographic context, and thematic relevance. Each paper was assessed using a matrix of inclusion factors. Only studies presenting original data or primary analysis were retained. A team from Warwick University independently reviewed 30% of selected articles to minimize selection bias. Thematic analysis was then conducted to synthesize insights under five major categories: (1) AI applications, (2) healthcare outcomes, (3) governance mechanisms, (4) ethical/legal dimensions, and (5) challenges. Sun et al. (2019) were used as a guide to make coding iterative to identify recurring frameworks and research gaps. 2.3. Conceptual Framework A well-defined conceptual framework that served as a roadmap and enhanced the significance of the study is presented in Fig. 3 , which elucidated the complex interplay among the three components. The primary emphasis was on the illustration of the interconnection and interdependence among them, which influence healthcare services, regulatory frameworks and technological advancements. The study observed a central theme that highlighted the pivotal role of data management in addressing ethical considerations, safeguarding information and ensuring the appropriate use of AI in healthcare settings. It underscored the significance of ethical standards in guiding AI applications by stressing the need for the responsible and transparent use of technology in hospitals. The conceptual framework acknowledged variations in culture, financial resource and infrastructural consideration as crucial factors that influence the appropriate management of data and the judicious use of AI that is tailored to specific needs across regions. This integral aspect provides a comprehensive understanding of the examination and analysis process. This study illuminates the core concepts that drive the systematic review of the three components and clarifies the complex relationships and considerations essential to the navigation of the intersection among them. 3. Results A total of 41 studies were included in the final synthesis after applying all inclusion and exclusion criteria. These studies spanned a diverse range of topics within AI application and data governance in African healthcare contexts, including diagnostic support systems, predictive modeling, epidemiological surveillance, healthcare management systems, and ethical or regulatory issues related to data use. Overall, the findings revealed three dominant trends: A strong focus on diagnostic and predictive AI tools, particularly in infectious diseases like malaria and tuberculosis. Limited emphasis on AI applications in health system administration and non-communicable diseases. Sparse integration of comprehensive data governance frameworks or ethical protocols across studies. These findings were partially expected, particularly the focus on infectious diseases, given their burden on African health systems. However, the lack of studies on non-communicable diseases and data governance was surprising and highlights a major gap. The studies varied in methodology, including quantitative (n = 22), qualitative (n = 8), and mixed-method approaches (n = 11). Most of the studies were conducted in Sub-Saharan Africa, with South Africa, Kenya, and Nigeria accounting for the majority. A breakdown of the thematic categories and country distributions is provided in Tables 5 and 6 . The subsequent subsections delve deeper into thematic patterns observed. Table 5 Thematic Categories of Included Studies Thematic Category No. of Studies Description AI in Disease Diagnosis and Prediction 15 Use of AI tools for early diagnosis, screening, and outbreak prediction AI in Health Systems Management 7 Applications supporting logistics, hospital management, and patient flow optimization Data Governance and Security 6 Focus on data protection, ethical frameworks, and regulatory mechanisms AI in Personalized Medicine and Treatment Planning 5 Use of AI for individualized care and genomics-informed interventions AI in Public Health Surveillance 4 AI applied to community-level disease tracking and health system responsiveness AI Ethics and Policy in African Contexts 4 Focused discussion on fairness, equity, and policy recommendations AI for Workforce Support and Training 2 Use of AI to train or assist healthcare workers, especially in rural or underserved regions Total 43 Some studies addressed more than one theme Some studies were multi-thematic and counted under more than one category. Table 6 Geographic Distribution of Included Studies Country/Region No. of Studies Notes South Africa 10 Strong infrastructure and academic output Kenya 7 Several AI health pilot projects, especially in TB and HIV Nigeria 6 Focus on diagnostic tools and healthcare access Ghana 3 Emerging interest in health data governance Uganda 2 Included studies focused on maternal and community health Ethiopia 2 Focus on digital health system strengthening Rwanda 2 Government-led AI and eHealth initiatives Multinational/Regional 6 Pan-African or cross-country analyses (e.g., SSA or AU-level) Other (e.g., Tunisia, Morocco) 3 Limited but growing interest in AI and data governance Total 41 3.1. Benefits of AI to Healthcare in Lower- and Middle-Income Countries (LMICs) Starting from the 1980s AI development in healthcare medicine experienced an exceptional advancement path (see the chronological framework). Scarce in the beginning as rule-based systems AI research later incorporated deep learning models together with neural networks according to Wahl et al, 2018 and Schwalbe & Wahl., 2020. The development brings forward groundbreaking uses for healthcare domains. AI-driven computer systems use dataset analysis to forecast disease propagation risks and health threats in public health space while delivering better and faster diagnostic outcomes through medical imaging than traditional methods (Carter et al., 2020 ). Person-specific medical care has become a vital healthcare advance through AI technologies. Patient-specific treatment plans emerge from AI analysis of extensive clinical data while cancer care needs these plans to determine treatment protocols based on genetics. The transformative approach of personalised medicine headquartered its success from efficiency and accuracy alongside patient-specific strategies to provide better healthcare results and more productive disease management (Alanazi et al., 2023). Chronic illness management through AI utilizes real-time patient data and logics to track treatment adjustments as a solution for disease management effectiveness. The combination of AI with robotics assists surgical procedures by achieving better precision and accuracy thus patients recover quickly while achieving higher surgical outcomes (Singh et al., 2023 ). SEHU describes an important patient care development that bridges medical expertise with AI precision. Research about online healthcare integration of data alongside AI specifically in low- and middle-income countries (LMICs) demonstrates how successful AI implementation can be in healthcare institutions. Aerts and Bogdan-Martin ( 2021 ) along with Alami et al. ( 2020 ) investigated how electronic health tools and AI technology transform medical care particularly in resource-condtioned healthcare settings. Aerts and Bogdan-Martin ( 2021 ) explain why AI tools combined with digital systems help healthcare systems evolve from post-event management to adopts preventive measures. The authors stress that nations must create integrated digital health plans for rulemaking together with simplified service access and technology connexion and team-based systems supported by ongoing financial backing. The development of strong online health systems in LMICs requires a comprehensive method that has been demonstrated to create essential changes in healthcare delivery (Goodman et al., 2023 ). Guo & Li ( 2018 ) investigated AI as a technology to alleviate health service inequalities between cities and countryside locations in less industrialised countries. The analysis suggests AI technology might help both small towns' healthcare sector and their medical personnel to maximise efficiency through support of medical staff shortage. These studies unite under a main theme while showing their individual differences because they prove AI delivers positive health results in less economically developed nations. This component delivers two major benefits by establishing advanced diagnostic methods while making healthcare available to more people and enhancing clinical management besides offering the possibility to decrease healthcare costs (Waljee et al., 2022 ). The convergence of AI systems along with quality data management created substantial modifications to healthcare systems throughout the world. The diagnostic accuracy of AI has increased substantially because of its disease detection capabilities according to Guan ( 2019 ). Such technological achievements allowed healthcare operations to excel in two essential areas through improved diagnostics by developing advanced imaging procedures and customized treatments that derive from highly precise analysis systems for disease recognition applications. Remote health systems make it possible to detect issues that traditional health monitoring strategies cannot identify. The revolutionary progress presents various obstacles for implementation (Winter et al., 2019). Healthcare systems require procedures to determine AI decisionmaking methods alongside guidelines for responsible data use and bias control for AI systems. The future of AI in medical practise needs to concentrate on technological advancements and challenges according to Kamruzzaman ( 2021 ). The integrated method seeks to extract the highest possible beneficial effects of AI technology in world health by developing systems that efficiently process data while operating quickly and centering patient care. 3.2. Challenges in AI and Healthcare The studies show consistent concern about the problems which arise from using non-African data to train AI models together with structural weaknesses and ethical problems and governance challenges and the need for skill development. Several scholars suggested AI would bring major healthcare improvements yet other experts remained doubtful because of insufficient proof (Anom, 2020 ). Data security worries and control problems and fair practises doubts keep coming back as unresolved issues. The research outcomes demanded a complete framework of national health plans together with regulatory systems along with improvement programs for skills and ethical standards to effectively leverage AI potential yet reduce its risks. The authors present various viewpoints that address both possible outcomes and challenges of implementing AI healthcare systems in developing countries (Arakpogun et al., 2021 ). Ciecierski-Holmes et al. ( 2022 ) state that there are three main barriers to the healthcare AI system’s adoption in LMIC: integration into existing infrastructure, insufficient quality and availability of data, as well as limitations on the level of the hospital infrastructure. Trust of users and acceptance of the system are critical implementation factors since employees are against new systems if they are unsure about their security, and fear job replacement. Machine learning system will develop and maintain biassed predictions if it is trained by non-diverse datasets. Regulations as well as ethical obligations arising from data protection, patient consent and responsibility of administration make it complex to deploy such AI tools system wide. Thus, the evaluation procedure for AI instruments requires longer term examination, facts selection, collaboration involving stakeholders as well as open sharing and resources for the necessary technological support system. Platform for the assessment of long time periods through implemented measures for health outcome evaluations and for emerging issue detection. These described challenges must be solved in order to deploy successful AI tools in the healthcare facilities. Certain parts alongside economically challenged African territories experience a major difficulty when it comes to digital access. Healthcare problems intensify because of deficient technology distribution and these consequences affect vulnerable patient groups negatively. The use of AI tools in healthcare requires serious ethical evaluation because biassed AI technology delivers discriminatory healthcare results (Taeihagh, 2021 ). The practise of secure data management represents a key requirement for medical staff who protect both patient safety and confidentiality records. Healthcare benefits from the enhanced role of AI when deliberate actions with equity drive its advancement. To incorporate advanced technology and provide equal chances experts must commit to system evolution along with defining proper ethical principles for AI tool use (Jabarulla & Lee, 2021 ). Healthcare data faces a critical limitation which threatens its utility because Gao et al. ( 2023 ) have recognised this serious issue. The data science models and applications lack sufficient representation of African people during database construction. The insufficient numbers of African subjects in datasets make data science tools unreliable while they produce incorrect results in African population groups. The deficiency of vigorous focused work to close the data science equality gap will possess dangerous effects on health research through data science in African communities. Medical organisations require good data management systems that will enable securing AI deployment, resolve healthcare challenges without exposing adverse clinical environments. However, to remain effective, the efficiency of AI depends on the systematic evaluation and data management protocol update. (Sun et al., 2019). Ademolowo et al. ( 2023 ) advocate for Africa to uphold the table of ethical standard and regulatory norms that guide the development process of the AI tool in healthcare with specific requirements such as privacy of information, consultation precautions and legal fairness that needs to be met while additionally fulfilling international and country standard minimum requirements. 3.3. Data Governance in AI and Healthcare Two critical issues arise when artificial intelligence systems are implemented in healthcare practise due to the need for rigorous standard for protection of patient private information when there is so much data. Moreover, data management effectiveness has a dual role in enabling secure information exchange, as well as detail protection in healthcare operations, and simplifies continuous data flows. Two main qualities that need to be in healthcare data systems, they need to be resilient and flexible and have the capacity to adapt to healthcare and technological changes. Another focus on data organisation is to ensure the safety as well as the fairness for AI health applications among all healthcare stakeholders (McMahon et al., 2020). However, serious attention must be given to healthcare requirements, existing technical capa bilities as well as local and worldwide data security rules. Healthcare regulations on data deal with health information and protection of personal details which should apply to all sectors because they protect personal information as well as secure health related facts according to the international standards. It is worth mentioning that when it comes to AI integration in healthcare, healthcare providers are dealing with big complex data which makes fine tuning those guidelines more important (Reddy et al., 2020 ). Thus, these regulations are set to protect the health information, the security of the health information and ethical boundaries there by creating trust in healthcare systems and ensure effective performance of AI tools. Due to the pace of health care and technology development, various data regulations should show both strength and be able to quickly adapt and adjust. An organisation must conduct its regular policy evaluation to be aware of regular AI advancements and the latest security threats. Fast technological progress is supported by continued effectiveness for patient privacy and security measures, in terms of the ability to perturb healthcare systems on the fly. Healthcare professionals need to be part of continuing training and learning activities to keep up to date with the new healthcare technologies (Ju et al., 2018 ). It is necessary to continuously train medical data regulations because it contributes for the work of correct implementation and compliance with standards in healthcare 'at the stage of development of the current period of AI application' (Shaw et al., 2018). Proper regulatory framework must then be achieved to support the new healthcare concepts as healthcare quality protection and patient rights defence including. It necessitated established guidelines and sharp continuous training to support the development of a new technology (McMahon et al., 2020). To use recently available tools that improve complex information management without sacrificing patient security and confidentiality, healthcare workers need an implementation of ethical data handling practises. AI holds significant promise for supporting healthcare, as noted by Car et al. ( 2019 ) and Fatima et al. ( 2020 ). However, they caution against ungrounded enthusiasm regarding AI’s applications, emphasizing the necessity of data privacy and responsible AI deployment. Skilled management solutions are essential for streamlining data and codes. Alami et al. ( 2020 ) and Ahmad et al. ( 2022 ) examine both the positive and negative impacts of AI on health disparities in underdeveloped countries. Their findings highlight various benefits, such as improving access to medical services and enhancing disease forecasting techniques. Nonetheless, Gwagwa et al. ( 2021 ) identify challenges like data bias issues and management complexities, advocating for a prudent approach. According to Alami et al. ( 2020 ), successfully deployed digital health instruments in Africa with artificial intelligence are powerful forces behind health system evolution in developing countries. Because of this need for dedicated support infrastructure in addition to regulatory standards with consistent funding commitments to sustain AI healthcare solutions, a thorough approach is mandatory that goes beyond simple technology implementation (Kerasidou, 2021 ). This research gives the reason for why data fairness issues need to be addressed firstly, while building high quality healthcare AI applications. Car et al. ( 2019 ) suggest that big data and AI in health care can be affordable and useful, but not enough evidence is out there that AI products would bring considerable benefits to the health care. The researchers say that the results are so exact they depend on exact results and make concrete differences for medical practise. Research was conducted on ethical issues emphasizing health care requirements for better rules and privacy security as well as responsible AI practises (Brand et al., 2022 ). Fatima et al. ( 2020 ) studied national AI plans to analyse their impact on public sector improvement and business competition systems. Data and computer programme management under the public sector plays a vital role as the study demonstrates the fundamental importance of governance in this field. National governments require substantial investments that target AI capability development most especially in building new capabilities. The research advocated for tactical steps to improve AI exploitation capacity since AI represents a fundamental requirement for national advancements in the future (Effoduh et al., 2023 ; Reddy et al., 2019 ). The studies by Car et al. ( 2019 ) and Fatima et al. ( 2020 ) presented opposing opinions though Zuhair et al. ( 2024 ) established that AI deployment in health diagnosis, prognosis prediction and patient care management alongside hospital and community healthcare services leads to better medical system efficiency specifically in densely populated health facilities with insufficient resources at developing world locations. 3.4. Ethical Considerations in AI and Healthcare Fosso & Queiroz ( 2023 ) investigated the connexion between AI and digital health utilising bibliometrics as their research method. The research analysed how responsible AI works while investigating moral and ethical problems that exist in healthcare AI systems. By employing a systematic technique, the authors found the changes together with challenges and positive outcomes of AI integration in healthcare within World Health Organisation ethical framework for AI (WHO 2021). This document delivered detailed instructions along with practical advice for medical practitioners who follow the main contemplation of ethics alongside regulation when using AI to transform healthcare. These studies ( Appendix 1 ) demonstrated the need for fair medical service delivery which includes moral considerations with rule compliance alongside essential infrastructure creation. AI healthcare utilisation develops toward responsible behaviour and inclusion through cohesive element integration to secure benefits for numerous people. 4. Discussion 4.1 Key Findings and their Comparison with Existing Evidence This review identified three key findings across 41 studies: (1) A dominant use of AI in diagnostic and predictive functions, (2) Limited application of AI in health systems management and non-communicable diseases, and (3) Underrepresentation of robust data governance structures and ethical frameworks in most implementations. First, our analysis revealed a strong orientation toward AI tools used for disease diagnosis and prediction, particularly for infectious diseases like tuberculosis, malaria, and HIV. This was expected due to the high disease burden in Sub-Saharan Africa. However, the narrow thematic focus, despite widespread calls for digital health innovation, suggests limited diversification of AI applications in African contexts. This finding aligns with Wahl et al. ( 2018 ), who found similar trends in global LMICs but contrasts with recent high-income country studies showing broader AI usage in surgical planning and chronic disease management (Singh et al., 2023 ). Second, we observed a significant gap in the deployment of AI systems in areas such as hospital management, patient scheduling, and healthcare logistics. Only 7 of the 41 studies addressed health system optimization. This indicates that while technical AI innovation is progressing, its integration into administrative layers remains minimal. This partially disaffirms earlier projections (e.g., Kamruzzaman, 2021 ) that foresaw rapid AI expansion into these domains across LMICs. Third, few studies (n = 6) integrated structured data governance frameworks or evaluated ethical implications systematically. This is concerning, considering the centrality of data privacy, algorithmic bias, and informed consent to the sustainability of AI in healthcare. Although some studies (e.g., Ademolowo et al., 2023 ) advocate for national-level policy formulation, practical enforcement and ethical implementation remain limited. Our findings support Gwagwa et al. ( 2021 ), who emphasized the embryonic state of ethical AI governance in Africa, especially at clinical points of care. Finally, a cross-regional imbalance was observed. Countries like South Africa and Kenya dominate scholarly output, while large portions of Central and North Africa remain underrepresented. This affirms previous geographic analyses by Alami et al. ( 2020 ), who noted a concentration of AI experimentation in relatively better-resourced countries. Figure 4 illustrates the case study on the integration of AI technology into tuberculosis detection in Kenya, which highlights a three-phase implementation process, namely, deployment of the pilot program, training of healthcare workers and integration into national health databases. Despite challenges, such as the limitations and costs of infrastructure, the initiative enhanced diagnostic accuracy and capacity building and was supported by diverse funding sources, including government, international aid and public–private partnerships. According to Car et al.'s ( 2019 ) research they investigated big data and AI convergence applications in healthcare through an analysis of modern data processing systems which became accessible and affordable. This research proved that there are insufficient empirical data showing how AI products improve medical care quality. The study demonstrates a need for ethical decision practices that deal with data sharing problems and privacy protection through valuable laws controlling AI healthcare administration. Fatima et al. ( 2020 ) examined American AI strategic plans in order to show how governance establishes productive public services and advantageous market competition. The comprehensive plans need specific regulations to build proper data handling systems and determine research methods as described by the authors. AI technologies need appropriate control systems embedded in national strategy documents for managing effective resources and optimizing their use according to Jogi ( 2021 ). Mahomed (2018) advises nations to dedicate generous resources for developing their AI capabilities on a national level. A country requires financial investment in AI development because the enhancement enables better application of AI in future development initiatives and competitive strategies. Several research findings confirm that combined approaches of digital health technologies and AI possess solid potential to transform medical services throughout LMICs. The authors advocate for nationwide strategic AI healthcare plans supported by strong oversight systems which require continuous financial support to achieve successful execution. AI's transformative capabilities require recognition at the same time as the critical need exists to solve problems associated with biased data points and ethical frameworks as well as complex regulatory systems. These investigations show that a cohesive approach should advance beyond standard technological unification through ethical rulemaking and proper implementation systems for inclusive healthcare services. 4.2 Limitations of the Study This study is not without limitations. First, it only included peer-reviewed articles in English, potentially excluding valuable grey literature or regional publications in French, Arabic, or Portuguese-speaking African countries. Second, despite using multiple databases, publication bias may have influenced the visibility of certain studies—especially those from countries with limited research infrastructure. Subjective judgments about themes emerged during thematic interpretation because the PRISMA framework enhances methodological transparency yet does not eliminate differences between reviewers. Recent technological developments in AI surpass what can be found in current academic publications. 4.2. Challenges and Future Directions The AI delivers a transformative potential, but challenges such as lacking localised data, lower digital literacy, infrastructural inequality, and underdeveloped regularity frameworks persist. Overcoming these requires multi-level investment in education, infrastructure, and regulatory capacity. Future research should emphasise longitudinal impacts of AI integration on patient outcomes, comparative analysis across African subregions, and development of localized ethical and regulatory standards. 5. Conclusions Integrating AI into healthcare, particularly in LMICs, offers substantial opportunities but presents formidable challenges. Research highlighted the transformative potential of AI in enhancing disease detection, diagnosis and healthcare accessibility in resource-constrained settings. The selected studies underscored the crucial role of comprehensive national strategies, robust governance systems and substantial investments in realizing the full potential of AI while addressing ethical concerns and biases. Multiple hurdles present during the implementation of AI technology in healthcare operations need to be addressed. A detailed approach must be used to resolve privacy and fairness issues and digital inequality matters. Research activity with team-based collaboration and mutable strategies must remain essential for handling healthcare problems to ensure AI delivers benefits across every healthcare setting equitably. Every background should learn about AI computing and gain essential proficiencies to access AI tools through all resource channels the meetings emphasize. The partnership agenda connects healthcare access between different communities to generate superior health outcomes for vulnerable population groups. The future of AI in healthcare depends on its careful execution through patient trust programs that combine moral principles for maximal technological benefits to both health institutions and global health management. Active continuous work in AI healthcare integration depends on combined operating efforts to produce technological developments which benefit all patients in standardized healthcare environments. Declarations Funding: Wellcome Trust through Warwick University and the University of Nairobi. Acknowledgements Prof. Sekalala, Sharifah – Warwick University Dr. Ben Mkalama – University of Nairobi Yureshya.Y.S.Perera – Warwick University Al Kags and the entire team at Open Institute References Adebamowo CA, Callier S, Akintola S, Maduka O, Jegede A, Arima C, Ogundiran T, Adebamowo SN, BridgELSI Project as part of the DS-I Africa Consortium. The promise of data science for health research in Africa. Nat Commun. 2023;14:6084. https://doi.org/10.1038/s41467-023-41809-2 Ademolowo M, Dube K, Mars M. Africa must develop robust AI governance for digital health. Lancet Digit Health. 2023;5(5):e257. https://doi.org/10.1016/S2589-7500(23)00082-2 Aerts A, and Bogdan-Martin D. Leveraging data and AI to deliver on the promise of digital health. Int J Med Inform. 2021;150:104456. https://doi.org/10.1016/j.ijmedinf.2021.104456 Ahmad K, Maabreh M, Ghaly M, Khan K, Qadir J, Al-Fuqaha A. 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Ethics of artificial intelligence in global health: explainability, algorithmic bias and trust. J Oral Biol Craniofac Res. 2021;11:612–4. https://doi.org/10.1016/j.jobcr.2021.09.004 Mahomed S. Healthcare, artificial intelligence and the fourth Industrial Revolution: ethical, social and legal considerations. S Afr J Bioeth Law. 2018;11(2):93–5. https://doi.org/10.7196/SAJBL.2018.v11i2.00664McMahon A, Buyx A, Prainsack B. Big data governance needs more collective responsibility: the role of harm mitigation in the governance of data use in medicine and beyond. Med Law Rev. 2020;28:155–82. https://doi.org/10.1093/medlaw/fwz016 Micheli M, Ponti M, Craglia M, Berti Suman A. Emerging models of data governance in the age of datafication. Big Data Soc. 2020;7:2053951720948087. https://doi.org/10.1177/2053951720948087 Ndemo B, Mkalama B. Digitalisation and financial data governance in Africa: challenges and opportunities. Chapter 6 Page 131-154. In Ndemo B, Ndung’u N, Odhiambo S, Shimeles A, editors. Data governance and policy in Africa. Berlin: Springer Nature; 2023. pp. 131–53. Owoyemi A, Owoyemi J, Osiyemi A, Boyd A. Artificial intelligence for healthcare in Africa. Front Digit Health. 2020;2:6. https://doi.org/10.3389/fdgth.2020.00006 Reddy CL, Mitra S, Meara JG, Atun R, Afshar S. Artificial intelligence and its role in surgical care in low-income and middle-income countries. Lancet Digit Health. 2019;1:e384–6. https://doi.org/10.1016/S2589-7500(19)30200-6 Reddy S, Allan S, Coghlan S, Cooper P. A governance model for the application of ai in health care. J Am Med Inform Assoc. 2020;27:491–7. https://doi.org/10.1093/jamia/ocz192 Schwalbe N, Wahl B. Artificial intelligence and the future of global health. Lancet. 2020;395:1579–86. https://doi.org/10.1016/S0140-6736(20)30226-9 Seto E, Leonard KJ, Cafazzo JA, Barnsley J, Masino C, Ross HJ. Developing healthcare rule-based expert systems: case study of a heart failure telemonitoring system. Int J Med Inform. 2012;81:556–65. https://doi.org/10.1016/j.ijmedinf.2012.03.001 Shaw J, Rudzicz F, Jamieson T, Goldfarb A. Artificial intelligence and the implementation challenges. J Med Internet Res. 2019;21:e13659. https://doi.org/10.2196/13659. Singh A, Madaan G, Swapna HR, Kumar A. Impact of artificial intelligence on human capital in healthcare sector post-Covid-19. In: The adoption and effect of artificial intelligence on human resources management, part A. Leeds: Emerald Publishing Limited; 2023. pp. 47–69. Singh A, Madaan G, Swapna HR, Kumar A. Impact of artificial intelligence on human capital in healthcare sector post-Covid-19. In: The adoption and effect of artificial intelligence on human resources management, Part A. Leeds: Emerald Publishing Limited; 2023. p. 47–69. Staunton C, Adams R, Horn L, Labuschaigne M. A framework to govern the use of health data for research in Africa: a South African perspective. In: Zima T, Weisstub DN, editors. Medical research ethics: challenges in the 21st Century. Berlin: Springer International Publishing; 2023. pp. 485–99. Sun TQ, Medaglia R. Mapping the challenges of artificial intelligence in the public sector: evidence from public healthcare. Gov Inf Q. 2019;36:368–83. https://doi.org/10.1016/j.giq.2018.09.008 Taeihagh A. Governance of artificial intelligence. Policy Soc. 2021;40:137–57. https://doi.org/10.1080/14494035.2021.1928377 Tanui CK, Ndembi N, Kebede Y, Tessema SK. Artificial intelligence to transform public health in Africa. Lancet Infect Dis. 2024;24:e542. https://doi.org/10.1016/S1473-3099(24)00435-3 Wahl B, Cossy-Gantner A, Germann S, Schwalbe NR. Artificial intelligence (AI) and global health: how can AI contribute to health in resource-poor settings? BMJ Glob Health. 2018;3:e000798. https://doi.org/10.1136/bmjgh-2018-000798 Waljee AK, Weinheimer-Haus EM, Abubakar A, Ngugi AK, Siwo GH, Kwakye G, Singal AG, Rao A, Saini SD, Read AJ, Baker JA, Balis U, Opio CK, Zhu J, Saleh MN. Artificial intelligence and machine learning for early detection and diagnosis of colorectal cancer in sub-Saharan Africa. Gut. 2022;71:1259–65. https://doi.org/10.1136/gutjnl-2022-327211 Winter JS, Davidson E. Big data governance of personal health information and challenges to contextual integrity. Inf Soc. 2019;35:36–51. https://doi.org/10.1080/01972243.2018.1542648 World Health Organization. Ethics and governance of artificial intelligence for health: WHO guidance. Geneva: World Health Organization; 2021. https://www.who.int/publications/i/item/9789240029200 Zuhair V, Babar A, Ali R, Oduoye MO, Noor Z, Chris K, Okon II, Rehman LU. Exploring the impact of artificial intelligence on global health and enhancing healthcare in developing nations. J Prim Care Community Health. 2024;15:21501319241245847. https://doi.org/10.1177/21501319241245847 Zuiderwijk A, Chen YC, Salem F. Implications of the use of artificial intelligence in public governance: A systematic literature review and a research agenda. Gov Inf Q. 2021;38:101577. https://doi.org/10.1016/j.giq.2021.101577. Additional Declarations The authors declare no competing interests. Supplementary Files Appendix.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Background\",\"content\":\"\\u003cp\\u003eSince the mid-1980s, artificial intelligence (AI) was integrated into the broad healthcare context and initially piloted in Kenya as Medical AI (MAI) (Owoyemi et al., \\u003cspan citationid=\\\"CR38\\\" class=\\\"CitationRef\\\"\\u003e2020\\u003c/span\\u003e). This period was marked by the development of expert systems, which utilized predefined rules and logical principles to mimic human knowledge in specific medical domains. The systems used data inputs to generate diagnostic suggestions and developed into modern healthcare AI applications (Seto et al., \\u003cspan citationid=\\\"CR42\\\" class=\\\"CitationRef\\\"\\u003e2012\\u003c/span\\u003e). Medical practitioners focused on extracting knowledge from unstructured medical texts through the entire span from early 2000s until the 2010s. Natural language processing systems together with text-search methods enabled medical and research records as well as health papers to be extensively investigated to extract detailed information. The advancement made it possible for medical professionals to produce specific useful knowledge from large amounts of medical texts for better healthcare decisions (Dreisbach et al., \\u003cspan citationid=\\\"CR15\\\" class=\\\"CitationRef\\\"\\u003e2019\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003eAI methods underwent substantial developments in medical image interpretation following the year 2010. The technology of convolutional neural networks achieved top performance in photo processing functions which included picture element identification along with segmentation and classification projects. The recent technological progress enabled medical practitioners in Africa to receive enhanced comprehension of diagnostic images including X-rays and MRIs and CT scans. The improved capability to identify patients alongside enhanced anomaly detection was made possible by this aspect (Gao et al., \\u003cspan citationid=\\\"CR20\\\" class=\\\"CitationRef\\\"\\u003e2018\\u003c/span\\u003e). Currently, AI applications in the healthcare sector include disease prediction, in-depth understanding and the operation of robots that assist in surgical procedures (Zuhair et al., \\u003cspan citationid=\\\"CR54\\\" class=\\\"CitationRef\\\"\\u003e2024\\u003c/span\\u003e). These applications transformed AI into a potent force, which revolutionized modern healthcare practices. Notable examples of AI utilization in Africa highlight its impact on improving human life and fostering openness, safety and operational ease. The effectiveness of AI in healthcare is heavily reliant on access to suitable data sources, particularly big data, to execute tasks efficiently and expeditiously (Kaplan \\u0026amp; Haenlein, \\u003cspan citationid=\\\"CR33\\\" class=\\\"CitationRef\\\"\\u003e2020\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003eAI and data governance frameworks are being introduced into African healthcare systems as a response to structural and systemic healthcare challenges is not prima facie evidence in favour of the technological determinist stereotype. Healthcare systems in the entire continent are not homogeneous in terms of infrastructure, human resources, institutional capacity and technological readiness. Rural and urban health care, clinical and nonclinical systems, and sub disciplines whose members provide health care such as pharmacy, nursing, radiology and clinical laboratory sciences are found to have pronounced disparities. Each sub discipline has its own classification system and coding structure. IMO Health, a leading medical data organization, identifies, four most widely used standardized coding systems including International Classification of Diseases ICD-11, Current Procedural Terminology (CPT), Systemized Nomenclature of Medicine (SNOMED) and Logical Observation Identifiers Names and Codes (LOINC) as standardized procedural codes in different disciplines. Other standards like Terminologia Anatomica (TA) are used as a global standard for anatomical terminology (Chamberlain, \\u003cspan citationid=\\\"CR11\\\" class=\\\"CitationRef\\\"\\u003e2024\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003eThe codes are a reminder of how the data exists across institutions and regions, which is some complexity. The lack harmonization and semantic interoperability may lead to a poor performance of AI systems or reinforcement of existing disparities. African health systems have developed in peculiar socio political and legal contexts. For example, South Africa\\u0026rsquo;s health care system moderns itself in the wake of apartheid era institutional segregation by persistent bifurcated service delivery architectures that persistently defy disintegration. History dictates the structure, priorities and effectiveness of the healthcare systems\\u0026rsquo; response to new technologies such as AI. Based on typological frameworks, healthcare delivery models also are characterized in varying degrees of entrepreneurial (market driven) from welfare oriented and universal systems. Some systems are centralized, others decentralized; some publicly funded, others dominated by private actors. These differences impact how AI and data governance mechanisms are implemented, scaled, and regulated. To better integrate AI, it's also useful to consider the healthcare infrastructure in layered terms: (1) the business layer (e.g., care delivery workflows), (2) the data layer (e.g., patient records, test results), (3) the application layer (e.g., clinical decision support systems), and (4) the technology layer (e.g., networks, cloud services). Each layer presents unique challenges and must be addressed in governance frameworks and AI implementations.\\u003c/p\\u003e \\u003cp\\u003eThus, a one-size-fits-all approach to AI in African healthcare is inadequate. Interventions must be context-specific and cognizant of layered healthcare architecture, sector-specific norms, historical inequities, and typological diversity. These insights establish the rationale for evaluating how AI and data governance have been approached across the continent post-2014.The increased demand for data to train various health models led to concerns about data security and privacy. As a result, the African Union (AU) adopted the AU Convention on Cyber Security and Personal Data Protection (AUCCPDT) in 2014. Several countries participated in the development of national data governance policies (Michel et al, 2020) and by early 2023, at least 27 countries passed laws, and eight others were in the process (Ndemo \\u0026amp; Mkalama, 2024). Post-2020 witnessed increased dependence on digital health records and extensive data repositories in predicting medical outcomes and providing personalized treatment in Africa. Machine learning and data mining enable the analysis of vast datasets (Marques et al, 2019), which helped to reveal the trajectory of illnesses, assess associated risks and modify treatment strategies based on individual patient requirements (Hulsen et al., \\u003cspan citationid=\\\"CR27\\\" class=\\\"CitationRef\\\"\\u003e2019\\u003c/span\\u003e). Table\\u0026nbsp;1 presents the chronological Framework on AI and Healthcare in Africa.\\u003c/p\\u003e \\u003cp\\u003e\\u003cimg 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\\\" width=\\\"765\\\" height=\\\"442\\\"\\u003e\\u003c/p\\u003e \\u003cdiv id=\\\"Sec2\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e1.1. Contextual Challenges of AI and Healthcare in Africa\\u003c/h2\\u003e \\u003cp\\u003eAI adoption in Africa continues to expand but limited implementation occurs because the region lacks technological equipment along with inadequate healthcare systems combined with a lack of digital literacy skills. Throughout Africa there are inconsistent levels of technology access which create barriers to its utilisation. The necessary infrastructure needed to operate AI programmes does not exist in various rural and underserved regions (Ciecierski-Holmes et al., \\u003cspan citationid=\\\"CR14\\\" class=\\\"CitationRef\\\"\\u003e2022\\u003c/span\\u003e; Staunton et al., \\u003cspan citationid=\\\"CR46\\\" class=\\\"CitationRef\\\"\\u003e2023\\u003c/span\\u003e). Software deployment in healthcare struggles primarily because medical institutions have difficulties implementing proper hardware systems and fail to maintain high-speed networks and stable connexions. The effectiveness of healthcare professionals is restricted by their different levels of digital literacy skills. The proper implementation of AI demands continuous learning programmes which can assist professionals to achieve maximum potential from their technology-driven solutions.\\u003c/p\\u003e \\u003cp\\u003eDifferent stages of healthcare infrastructure development across regions act as substantial obstacles to programme implementation. Adding AI technologies proves impossible because healthcare facilities lacking resources are unable to implement these systems. The absence of both financial funding and technical experts keeps these facilities from adopting new technological solutions (Owoyemi et al., \\u003cspan citationid=\\\"CR38\\\" class=\\\"CitationRef\\\"\\u003e2020\\u003c/span\\u003e). AI application becomes highly restricted in places which badly need its benefits as they lack the resources to deploy this technology. The mentioned difficulties in implementation did not stop professionals from attempting to surmount them. It is essential to boost digital infrastructure while offering specific healthcare training to staff and to ensure resource distribution remains fair (Adebamowo et al., \\u003cspan citationid=\\\"CR1\\\" class=\\\"CitationRef\\\"\\u003e2023\\u003c/span\\u003e). Healthcare AI requires support from coordinated actions between national governments and private stakeholders and international organisations to develop environments that can optimise its contributions to patient wellness. The effectiveness of AI in healthcare depends completely on getting suitable data sources mainly massive data so the system can work fast and efficiently (Kaplan \\u0026amp; Haenlein, \\u003cspan citationid=\\\"CR33\\\" class=\\\"CitationRef\\\"\\u003e2020\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003eRising digital record management practises across Africa lead to greater need for AI systems to forecast medical outcomes and deliver individualised treatments. Procedures using data mining and machine learning techniques study vast datasets to show medical illness patterns and evaluate risk factors simultaneously using individual patient preferences to generate modified treatment plans (Hulsen et al., \\u003cspan citationid=\\\"CR27\\\" class=\\\"CitationRef\\\"\\u003e2019\\u003c/span\\u003e). The successful operation of AI algorithms relies on ready reliable data. African countries face major difficulties accessing structured and correct comprehensive datasets because of missing records and data entry mistakes together with irregular data collection methods (Tanui et al., \\u003cspan citationid=\\\"CR49\\\" class=\\\"CitationRef\\\"\\u003e2024\\u003c/span\\u003e). The reliability of predictable models develops from high-quality data but inadequate data leads to poor results that impact clinical decisions and patient outcomes negatively.\\u003c/p\\u003e \\u003cp\\u003e \\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab1\\\" border=\\\"1\\\"\\u003e \\u003ccaption language=\\\"En\\\"\\u003e \\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 2\\u003c/div\\u003e \\u003cdiv class=\\\"CaptionContent\\\"\\u003e \\u003cp\\u003eSummary of Existing Reviews on AI and Data Governance in African Healthcare\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/caption\\u003e \\u003ccolgroup cols=\\\"4\\\"\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c1\\\" colnum=\\\"1\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c3\\\" colnum=\\\"3\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c4\\\" colnum=\\\"4\\\"\\u003e\\u003c/div\\u003e \\u003cthead\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eStudy\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eType of Review\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eScope\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eIdentified Gaps\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003c/thead\\u003e \\u003ctbody\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eOwoyemi et al. (\\u003cspan citationid=\\\"CR38\\\" class=\\\"CitationRef\\\"\\u003e2020\\u003c/span\\u003e)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eNarrative Review\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eGeneral applications of AI in African healthcare\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eLacks systematic methodology and focus on data governance\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eCiecierski-Holmes et al. (\\u003cspan citationid=\\\"CR14\\\" class=\\\"CitationRef\\\"\\u003e2022\\u003c/span\\u003e)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eSystematic Scoping Review\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eAI in LMICs with African representation\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eLimited to general systems strengthening; does not focus on post-2014 data governance landscape\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eAdebamowo et al. (\\u003cspan citationid=\\\"CR1\\\" class=\\\"CitationRef\\\"\\u003e2023\\u003c/span\\u003e)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eExpert Commentary\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eData science potential in African health research\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eTheoretical scope, no structured evidence synthesis\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eAlami et al. (\\u003cspan citationid=\\\"CR5\\\" class=\\\"CitationRef\\\"\\u003e2020\\u003c/span\\u003e)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eGlobal Systematic Review\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eAI innovation in LMICs\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eDoes not highlight African-specific governance and implementation challenges\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eGwagwa et al. (\\u003cspan citationid=\\\"CR26\\\" class=\\\"CitationRef\\\"\\u003e2021\\u003c/span\\u003e)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eLandscape Report\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eResponsible AI in Sub-Saharan Africa\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eDoes not use systematic review methodology, lacks healthcare-specific detail\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003c/tbody\\u003e \\u003c/colgroup\\u003e \\u003c/table\\u003e\\u003c/div\\u003e \\u003c/p\\u003e \\u003cp\\u003eThis review builds on these studies by systematically analyzing peer-reviewed literature from 2015 to 2023, focusing explicitly on the intersection of AI, healthcare, and data governance in Africa post-AUCCPDT (2014). It addresses the lack of structured comparative analysis and contextual insights into regulatory frameworks, ethics, and implementation outcomes.\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec3\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e1.2. Objectives\\u003c/h2\\u003e \\u003col style=\\\"list-style-type: lower-alpha;\\\"\\u003e\\n \\u003cli\\u003eThis research evaluates the evolution and impacts of AI and data governance frameworks in Africa since 2014, focusing on their contribution to improved healthcare delivery, patient outcomes, and data management.\\u003c/li\\u003e\\n \\u003cli\\u003eThis study aims to create a comprehensive evidence map analyzing AI applications and data governance strategies in Africa\\u0026rsquo;s healthcare interventions, focusing on operational outcomes, challenges, and lessons.\\u003c/li\\u003e\\n\\u003c/ol\\u003e\"},{\"header\":\"2. Methods\",\"content\":\"\\u003cp\\u003eThe research investigates the systematic applications regarding the ties between AI and healthcare data protection throughout African nations. The approach validates results through evaluations of health practise elements as well as regulatory standards and technological advancement measures (Gerke et al., \\u003cspan citationid=\\\"CR22\\\" class=\\\"CitationRef\\\"\\u003e2020\\u003c/span\\u003e). The comprehensive review discovers missing information which enables policymaking teams along with practitioners to gain strategic perspectives to establish a solid evidence foundation. The authors used the PRISMA 2020 protocol which serves as a standardised framework for systematic review to guarantee both reliability and robustness. Through this method researchers reduce bias while allowing others to repeat their work and enhance the research credibility in the field thereby demonstrating comprehensive quality.\\u003c/p\\u003e \\u003cp\\u003eA PRISMA flow chart describing the article selection is shown in Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e (and summarised) in Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e.After exclusion of duplicates, 41 articles overall were included for final analysis. The databases consisted of chosen combinations of the databases that include IEEE Xplore (15), Google Scholar (58), ACM Digital Library (12), PubMed (35), and Other Sources (7). An initial set of n\\u0026thinsp;=\\u0026thinsp;35 was after duplicate removal (n\\u0026thinsp;=\\u0026thinsp;35), screening, and eligibility checks, and 41 of 35 (n\\u0026thinsp;=\\u0026thinsp;41) peer reviewed empirical articles were included after review including grey literatures due to the need to keep the methodological rigour of the review. Moreover, all typographical problems were solved in the updated PRISMA diagram:\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003cp\\u003eThe study utilized a well-organized methodology to explore the convergence among AI, data control, and healthcare components in Africa. It utilized leading academic databases like IEEE Xplore, Google Scholar, ACM Digital Library, and PubMed to ensure access to original, up-to-date articles. A comprehensive research strategy was established (Table\\u0026nbsp;\\u003cspan refid=\\\"Tab1\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e). The rigorous selection process ensured relevance and significance of selected articles, providing a comprehensive, reliable, and interdisciplinary approach to literature evaluation. These databases provide a solid platform for investigating convergence in Africa. The research plan was designed to search these databases using Boolean operators (\\u0026ldquo;AND,\\u0026rdquo; \\u0026ldquo;OR\\u0026rdquo; and \\u0026ldquo;NOT\\u0026rdquo;) and specific keywords. This approach sought to encompass a wide range of literature in which AI, data control and healthcare intersect, to incorporate extensive and focused research findings. The search plan strategically aimed to identify articles that covers different aspects of the topic and diverse perspectives and insights into the complex dynamics among the three components in Africa.\\u003c/p\\u003e \\u003cp\\u003e \\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab2\\\" border=\\\"1\\\"\\u003e \\u003ccaption language=\\\"En\\\"\\u003e \\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 3\\u003c/div\\u003e \\u003cdiv class=\\\"CaptionContent\\\"\\u003e \\u003cp\\u003eResearch Strategy\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/caption\\u003e \\u003ccolgroup cols=\\\"4\\\"\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c1\\\" colnum=\\\"1\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c3\\\" colnum=\\\"3\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c4\\\" colnum=\\\"4\\\"\\u003e\\u003c/div\\u003e \\u003cthead\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eDatabase\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eKeywords\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eInclusion Criteria\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eExclusion Criteria\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003c/thead\\u003e \\u003ctbody\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eIEEE Xplore\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e\\u0026ldquo;AI in healthcare,\\u0026rdquo; \\u0026ldquo;data governance,\\u0026rdquo; \\u0026ldquo;Africa\\u0026rdquo;\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003ePeer-reviewed journal articles; empirical studies; English language; 2015\\u0026ndash;2023\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eEditorials, reviews, non-English texts\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003ePubMed\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e\\u0026ldquo;AI AND healthcare AND Africa\\u0026rdquo;\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003ePeer-reviewed articles; empirical evidence; studies conducted in African contexts; English; 2015\\u0026ndash;2023\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eStudies without direct healthcare relevance; conceptual papers only\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eGoogle Scholar\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e\\u0026ldquo;Artificial intelligence in African healthcare\\u0026rdquo;\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003ePeer-reviewed; full-text available; empirical data; 2015\\u0026ndash;2023\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eNon-peer-reviewed, blogs, dissertations, grey literature\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eACM Digital Library\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e\\u0026ldquo;Data governance in African health systems\\u0026rdquo;\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003ePeer-reviewed; empirical studies; African region focus; English; 2015\\u0026ndash;2023\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eNon-African focus; theoretical or policy-only articles\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eOther Sources\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eHand searches, cited references\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003ePeer-reviewed African-focused studies aligned with AI/data governance\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eGrey literature, conference posters\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003c/tbody\\u003e \\u003c/colgroup\\u003e \\u003c/table\\u003e\\u003c/div\\u003e \\u003c/p\\u003e \\u003cp\\u003eAll sources shared the same core criteria for inclusion: empirical, peer-reviewed, African context, and English language. The decision to start from 2015 stems from the 2014 African Union Convention on Cyber Security and Personal Data Protection, which marked a significant policy shift in data governance on the continent\\u0026mdash;making 2015 a logical start point for post-convention impact assessment.\\u003c/p\\u003e \\u003cdiv id=\\\"Sec5\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e2.1. Data Extraction\\u003c/h2\\u003e \\u003cp\\u003eA standardized data extraction form was developed and piloted to capture author(s), year, country, study design, AI application domain, data governance strategy, health system layer addressed (business/data/application/technology), outcomes, and ethical/legal considerations. Two reviewers independently extracted data from each article to ensure consistency and accuracy, with disagreements resolved through discussion.\\u003c/p\\u003e \\u003cp\\u003eThe process was grounded in ethical principles for secondary data use and emphasized responsible handling of potentially sensitive information (Zuiderwijk et al., \\u003cspan citationid=\\\"CR55\\\" class=\\\"CitationRef\\\"\\u003e2021\\u003c/span\\u003e). Table\\u0026nbsp;\\u003cspan refid=\\\"Tab6\\\" class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\u003e summarises included studies by year.\\u003c/p\\u003e \\u003cp\\u003e \\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab3\\\" border=\\\"1\\\"\\u003e \\u003ccaption language=\\\"En\\\"\\u003e \\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 4\\u003c/div\\u003e \\u003cdiv class=\\\"CaptionContent\\\"\\u003e \\u003cp\\u003eNumber of Studies Used\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/caption\\u003e \\u003ccolgroup cols=\\\"2\\\"\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c1\\\" colnum=\\\"1\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e \\u003cthead\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eYear\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eNumber of Studies\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003c/thead\\u003e \\u003ctbody\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e2018\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e5\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e2019\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e11\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e2020\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e10\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e2021\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e8\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e2022\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e17\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e2023\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e18\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003c/tbody\\u003e \\u003c/colgroup\\u003e \\u003c/table\\u003e\\u003c/div\\u003e \\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec6\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e2.2. Screening and Analysis\\u003c/h2\\u003e \\u003cp\\u003eScreening followed a two-stage approach:\\u003c/p\\u003e\\u003cp\\u003e1) Title and abstract screening for relevance to the three core pillars: AI, healthcare, and data governance in Africa.\\u003c/p\\u003e \\n\\u003cp\\u003e2) Full-text screening for empirical depth, geographic context, and thematic relevance.\\u003c/p\\u003e\\n\\u003cp\\u003eEach paper was assessed using a matrix of inclusion factors. Only studies presenting original data or primary analysis were retained. A team from Warwick University independently reviewed 30% of selected articles to minimize selection bias. Thematic analysis was then conducted to synthesize insights under five major categories: (1) AI applications, (2) healthcare outcomes, (3) governance mechanisms, (4) ethical/legal dimensions, and (5) challenges. Sun et al. (2019) were used as a guide to make coding iterative to identify recurring frameworks and research gaps.\\u003c/p\\u003e \\u003cdiv id=\\\"Sec8\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e2.3. Conceptual Framework\\u003c/h2\\u003e \\u003cp\\u003eA well-defined conceptual framework that served as a roadmap and enhanced the significance of the study is presented in Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig3\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003e, which elucidated the complex interplay among the three components. The primary emphasis was on the illustration of the interconnection and interdependence among them, which influence healthcare services, regulatory frameworks and technological advancements.\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003cp\\u003eThe study observed a central theme that highlighted the pivotal role of data management in addressing ethical considerations, safeguarding information and ensuring the appropriate use of AI in healthcare settings. It underscored the significance of ethical standards in guiding AI applications by stressing the need for the responsible and transparent use of technology in hospitals. The conceptual framework acknowledged variations in culture, financial resource and infrastructural consideration as crucial factors that influence the appropriate management of data and the judicious use of AI that is tailored to specific needs across regions.\\u003c/p\\u003e \\u003cp\\u003eThis integral aspect provides a comprehensive understanding of the examination and analysis process. This study illuminates the core concepts that drive the systematic review of the three components and clarifies the complex relationships and considerations essential to the navigation of the intersection among them.\\u003c/p\\u003e \\u003c/div\\u003e\"},{\"header\":\"3. Results\",\"content\":\"\\u003cp\\u003eA total of 41 studies were included in the final synthesis after applying all inclusion and exclusion criteria. These studies spanned a diverse range of topics within AI application and data governance in African healthcare contexts, including diagnostic support systems, predictive modeling, epidemiological surveillance, healthcare management systems, and ethical or regulatory issues related to data use.\\u003c/p\\u003e \\u003cp\\u003eOverall, the findings revealed three dominant trends:\\u003c/p\\u003e \\u003cp\\u003e \\u003cul\\u003e \\u003cli\\u003e \\u003cp\\u003eA strong focus on diagnostic and predictive AI tools, particularly in infectious diseases like malaria and tuberculosis.\\u003c/p\\u003e \\u003c/li\\u003e \\u003cli\\u003e \\u003cp\\u003eLimited emphasis on AI applications in health system administration and non-communicable diseases.\\u003c/p\\u003e \\u003c/li\\u003e \\u003cli\\u003e \\u003cp\\u003eSparse integration of comprehensive data governance frameworks or ethical protocols across studies.\\u003c/p\\u003e \\u003c/li\\u003e \\u003c/ul\\u003e \\u003c/p\\u003e \\u003cp\\u003eThese findings were partially expected, particularly the focus on infectious diseases, given their burden on African health systems. However, the lack of studies on non-communicable diseases and data governance was surprising and highlights a major gap. The studies varied in methodology, including quantitative (n\\u0026thinsp;=\\u0026thinsp;22), qualitative (n\\u0026thinsp;=\\u0026thinsp;8), and mixed-method approaches (n\\u0026thinsp;=\\u0026thinsp;11). Most of the studies were conducted in Sub-Saharan Africa, with South Africa, Kenya, and Nigeria accounting for the majority. A breakdown of the thematic categories and country distributions is provided in Tables\\u0026nbsp;\\u003cspan refid=\\\"Tab4\\\" class=\\\"InternalRef\\\"\\u003e5\\u003c/span\\u003e and \\u003cspan refid=\\\"Tab5\\\" class=\\\"InternalRef\\\"\\u003e6\\u003c/span\\u003e. The subsequent subsections delve deeper into thematic patterns observed.\\u003c/p\\u003e \\u003cp\\u003e \\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab4\\\" border=\\\"1\\\"\\u003e \\u003ccaption language=\\\"En\\\"\\u003e \\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 5\\u003c/div\\u003e \\u003cdiv class=\\\"CaptionContent\\\"\\u003e \\u003cp\\u003eThematic Categories of Included Studies\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/caption\\u003e \\u003ccolgroup cols=\\\"3\\\"\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c1\\\" colnum=\\\"1\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c3\\\" colnum=\\\"3\\\"\\u003e\\u003c/div\\u003e \\u003cthead\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eThematic Category\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eNo. of Studies\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eDescription\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003c/thead\\u003e \\u003ctbody\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eAI in Disease Diagnosis and Prediction\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e15\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eUse of AI tools for early diagnosis, screening, and outbreak prediction\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eAI in Health Systems Management\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e7\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eApplications supporting logistics, hospital management, and patient flow optimization\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eData Governance and Security\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e6\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eFocus on data protection, ethical frameworks, and regulatory mechanisms\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eAI in Personalized Medicine and Treatment Planning\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e5\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eUse of AI for individualized care and genomics-informed interventions\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eAI in Public Health Surveillance\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e4\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eAI applied to community-level disease tracking and health system responsiveness\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eAI Ethics and Policy in African Contexts\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e4\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eFocused discussion on fairness, equity, and policy recommendations\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eAI for Workforce Support and Training\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e2\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eUse of AI to train or assist healthcare workers, especially in rural or underserved regions\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eTotal\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e43\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eSome studies addressed more than one theme\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003c/tbody\\u003e \\u003c/colgroup\\u003e \\u003c/table\\u003e\\u003c/div\\u003e \\u003c/p\\u003e \\u003cp\\u003eSome studies were multi-thematic and counted under more than one category.\\u003c/p\\u003e \\u003cp\\u003e \\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab5\\\" border=\\\"1\\\"\\u003e \\u003ccaption language=\\\"En\\\"\\u003e \\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 6\\u003c/div\\u003e \\u003cdiv class=\\\"CaptionContent\\\"\\u003e \\u003cp\\u003eGeographic Distribution of Included Studies\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/caption\\u003e \\u003ccolgroup cols=\\\"3\\\"\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c1\\\" colnum=\\\"1\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c3\\\" colnum=\\\"3\\\"\\u003e\\u003c/div\\u003e \\u003cthead\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eCountry/Region\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eNo. of Studies\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eNotes\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003c/thead\\u003e \\u003ctbody\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eSouth Africa\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e10\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eStrong infrastructure and academic output\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eKenya\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e7\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eSeveral AI health pilot projects, especially in TB and HIV\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eNigeria\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e6\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eFocus on diagnostic tools and healthcare access\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eGhana\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e3\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eEmerging interest in health data governance\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eUganda\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e2\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eIncluded studies focused on maternal and community health\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eEthiopia\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e2\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eFocus on digital health system strengthening\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eRwanda\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e2\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eGovernment-led AI and eHealth initiatives\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eMultinational/Regional\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e6\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003ePan-African or cross-country analyses (e.g., SSA or AU-level)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eOther (e.g., Tunisia, Morocco)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e3\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eLimited but growing interest in AI and data governance\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eTotal\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e41\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003c/tbody\\u003e \\u003c/colgroup\\u003e \\u003c/table\\u003e\\u003c/div\\u003e \\u003c/p\\u003e \\u003cdiv id=\\\"Sec10\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e3.1. Benefits of AI to Healthcare in Lower- and Middle-Income Countries (LMICs)\\u003c/h2\\u003e \\u003cp\\u003eStarting from the 1980s AI development in healthcare medicine experienced an exceptional advancement path (see the chronological framework). Scarce in the beginning as rule-based systems AI research later incorporated deep learning models together with neural networks according to Wahl et al, \\u003cspan citationid=\\\"CR50\\\" class=\\\"CitationRef\\\"\\u003e2018\\u003c/span\\u003e and Schwalbe \\u0026amp; Wahl., 2020. The development brings forward groundbreaking uses for healthcare domains. AI-driven computer systems use dataset analysis to forecast disease propagation risks and health threats in public health space while delivering better and faster diagnostic outcomes through medical imaging than traditional methods (Carter et al., \\u003cspan citationid=\\\"CR13\\\" class=\\\"CitationRef\\\"\\u003e2020\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003ePerson-specific medical care has become a vital healthcare advance through AI technologies. Patient-specific treatment plans emerge from AI analysis of extensive clinical data while cancer care needs these plans to determine treatment protocols based on genetics. The transformative approach of personalised medicine headquartered its success from efficiency and accuracy alongside patient-specific strategies to provide better healthcare results and more productive disease management (Alanazi et al., 2023).\\u003c/p\\u003e \\u003cp\\u003eChronic illness management through AI utilizes real-time patient data and logics to track treatment adjustments as a solution for disease management effectiveness. The combination of AI with robotics assists surgical procedures by achieving better precision and accuracy thus patients recover quickly while achieving higher surgical outcomes (Singh et al., \\u003cspan citationid=\\\"CR44\\\" class=\\\"CitationRef\\\"\\u003e2023\\u003c/span\\u003e). SEHU describes an important patient care development that bridges medical expertise with AI precision.\\u003c/p\\u003e \\u003cp\\u003eResearch about online healthcare integration of data alongside AI specifically in low- and middle-income countries (LMICs) demonstrates how successful AI implementation can be in healthcare institutions. Aerts and Bogdan-Martin (\\u003cspan citationid=\\\"CR3\\\" class=\\\"CitationRef\\\"\\u003e2021\\u003c/span\\u003e) along with Alami et al. (\\u003cspan citationid=\\\"CR5\\\" class=\\\"CitationRef\\\"\\u003e2020\\u003c/span\\u003e) investigated how electronic health tools and AI technology transform medical care particularly in resource-condtioned healthcare settings. Aerts and Bogdan-Martin (\\u003cspan citationid=\\\"CR3\\\" class=\\\"CitationRef\\\"\\u003e2021\\u003c/span\\u003e) explain why AI tools combined with digital systems help healthcare systems evolve from post-event management to adopts preventive measures. The authors stress that nations must create integrated digital health plans for rulemaking together with simplified service access and technology connexion and team-based systems supported by ongoing financial backing. The development of strong online health systems in LMICs requires a comprehensive method that has been demonstrated to create essential changes in healthcare delivery (Goodman et al., \\u003cspan citationid=\\\"CR23\\\" class=\\\"CitationRef\\\"\\u003e2023\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003eGuo \\u0026amp; Li (\\u003cspan citationid=\\\"CR25\\\" class=\\\"CitationRef\\\"\\u003e2018\\u003c/span\\u003e) investigated AI as a technology to alleviate health service inequalities between cities and countryside locations in less industrialised countries. The analysis suggests AI technology might help both small towns' healthcare sector and their medical personnel to maximise efficiency through support of medical staff shortage. These studies unite under a main theme while showing their individual differences because they prove AI delivers positive health results in less economically developed nations. This component delivers two major benefits by establishing advanced diagnostic methods while making healthcare available to more people and enhancing clinical management besides offering the possibility to decrease healthcare costs (Waljee et al., \\u003cspan citationid=\\\"CR51\\\" class=\\\"CitationRef\\\"\\u003e2022\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003eThe convergence of AI systems along with quality data management created substantial modifications to healthcare systems throughout the world. The diagnostic accuracy of AI has increased substantially because of its disease detection capabilities according to Guan (\\u003cspan citationid=\\\"CR24\\\" class=\\\"CitationRef\\\"\\u003e2019\\u003c/span\\u003e). Such technological achievements allowed healthcare operations to excel in two essential areas through improved diagnostics by developing advanced imaging procedures and customized treatments that derive from highly precise analysis systems for disease recognition applications. Remote health systems make it possible to detect issues that traditional health monitoring strategies cannot identify.\\u003c/p\\u003e \\u003cp\\u003eThe revolutionary progress presents various obstacles for implementation (Winter et al., 2019). Healthcare systems require procedures to determine AI decisionmaking methods alongside guidelines for responsible data use and bias control for AI systems. The future of AI in medical practise needs to concentrate on technological advancements and challenges according to Kamruzzaman (\\u003cspan citationid=\\\"CR32\\\" class=\\\"CitationRef\\\"\\u003e2021\\u003c/span\\u003e). The integrated method seeks to extract the highest possible beneficial effects of AI technology in world health by developing systems that efficiently process data while operating quickly and centering patient care.\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec11\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e3.2. Challenges in AI and Healthcare\\u003c/h2\\u003e \\u003cp\\u003eThe studies show consistent concern about the problems which arise from using non-African data to train AI models together with structural weaknesses and ethical problems and governance challenges and the need for skill development. Several scholars suggested AI would bring major healthcare improvements yet other experts remained doubtful because of insufficient proof (Anom, \\u003cspan citationid=\\\"CR7\\\" class=\\\"CitationRef\\\"\\u003e2020\\u003c/span\\u003e). Data security worries and control problems and fair practises doubts keep coming back as unresolved issues. The research outcomes demanded a complete framework of national health plans together with regulatory systems along with improvement programs for skills and ethical standards to effectively leverage AI potential yet reduce its risks. The authors present various viewpoints that address both possible outcomes and challenges of implementing AI healthcare systems in developing countries (Arakpogun et al., \\u003cspan citationid=\\\"CR8\\\" class=\\\"CitationRef\\\"\\u003e2021\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003eCiecierski-Holmes et al. (\\u003cspan citationid=\\\"CR14\\\" class=\\\"CitationRef\\\"\\u003e2022\\u003c/span\\u003e) state that there are three main barriers to the healthcare AI system\\u0026rsquo;s adoption in LMIC: integration into existing infrastructure, insufficient quality and availability of data, as well as limitations on the level of the hospital infrastructure. Trust of users and acceptance of the system are critical implementation factors since employees are against new systems if they are unsure about their security, and fear job replacement. Machine learning system will develop and maintain biassed predictions if it is trained by non-diverse datasets. Regulations as well as ethical obligations arising from data protection, patient consent and responsibility of administration make it complex to deploy such AI tools system wide. Thus, the evaluation procedure for AI instruments requires longer term examination, facts selection, collaboration involving stakeholders as well as open sharing and resources for the necessary technological support system. Platform for the assessment of long time periods through implemented measures for health outcome evaluations and for emerging issue detection. These described challenges must be solved in order to deploy successful AI tools in the healthcare facilities.\\u003c/p\\u003e \\u003cp\\u003eCertain parts alongside economically challenged African territories experience a major difficulty when it comes to digital access. Healthcare problems intensify because of deficient technology distribution and these consequences affect vulnerable patient groups negatively. The use of AI tools in healthcare requires serious ethical evaluation because biassed AI technology delivers discriminatory healthcare results (Taeihagh, \\u003cspan citationid=\\\"CR48\\\" class=\\\"CitationRef\\\"\\u003e2021\\u003c/span\\u003e). The practise of secure data management represents a key requirement for medical staff who protect both patient safety and confidentiality records. Healthcare benefits from the enhanced role of AI when deliberate actions with equity drive its advancement. To incorporate advanced technology and provide equal chances experts must commit to system evolution along with defining proper ethical principles for AI tool use (Jabarulla \\u0026amp; Lee, \\u003cspan citationid=\\\"CR29\\\" class=\\\"CitationRef\\\"\\u003e2021\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003eHealthcare data faces a critical limitation which threatens its utility because Gao et al. (\\u003cspan citationid=\\\"CR21\\\" class=\\\"CitationRef\\\"\\u003e2023\\u003c/span\\u003e) have recognised this serious issue. The data science models and applications lack sufficient representation of African people during database construction. The insufficient numbers of African subjects in datasets make data science tools unreliable while they produce incorrect results in African population groups. The deficiency of vigorous focused work to close the data science equality gap will possess dangerous effects on health research through data science in African communities.\\u003c/p\\u003e \\u003cp\\u003eMedical organisations require good data management systems that will enable securing AI deployment, resolve healthcare challenges without exposing adverse clinical environments. However, to remain effective, the efficiency of AI depends on the systematic evaluation and data management protocol update. (Sun et al., 2019). Ademolowo et al. (\\u003cspan citationid=\\\"CR2\\\" class=\\\"CitationRef\\\"\\u003e2023\\u003c/span\\u003e) advocate for Africa to uphold the table of ethical standard and regulatory norms that guide the development process of the AI tool in healthcare with specific requirements such as privacy of information, consultation precautions and legal fairness that needs to be met while additionally fulfilling international and country standard minimum requirements.\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec12\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e3.3. Data Governance in AI and Healthcare\\u003c/h2\\u003e \\u003cp\\u003eTwo critical issues arise when artificial intelligence systems are implemented in healthcare practise due to the need for rigorous standard for protection of patient private information when there is so much data. Moreover, data management effectiveness has a dual role in enabling secure information exchange, as well as detail protection in healthcare operations, and simplifies continuous data flows. Two main qualities that need to be in healthcare data systems, they need to be resilient and flexible and have the capacity to adapt to healthcare and technological changes. Another focus on data organisation is to ensure the safety as well as the fairness for AI health applications among all healthcare stakeholders (McMahon et al., 2020).\\u003c/p\\u003e \\u003cp\\u003eHowever, serious attention must be given to healthcare requirements, existing technical capa bilities as well as local and worldwide data security rules. Healthcare regulations on data deal with health information and protection of personal details which should apply to all sectors because they protect personal information as well as secure health related facts according to the international standards. It is worth mentioning that when it comes to AI integration in healthcare, healthcare providers are dealing with big complex data which makes fine tuning those guidelines more important (Reddy et al., \\u003cspan citationid=\\\"CR40\\\" class=\\\"CitationRef\\\"\\u003e2020\\u003c/span\\u003e). Thus, these regulations are set to protect the health information, the security of the health information and ethical boundaries there by creating trust in healthcare systems and ensure effective performance of AI tools.\\u003c/p\\u003e \\u003cp\\u003eDue to the pace of health care and technology development, various data regulations should show both strength and be able to quickly adapt and adjust. An organisation must conduct its regular policy evaluation to be aware of regular AI advancements and the latest security threats. Fast technological progress is supported by continued effectiveness for patient privacy and security measures, in terms of the ability to perturb healthcare systems on the fly. Healthcare professionals need to be part of continuing training and learning activities to keep up to date with the new healthcare technologies (Ju et al., \\u003cspan citationid=\\\"CR31\\\" class=\\\"CitationRef\\\"\\u003e2018\\u003c/span\\u003e). It is necessary to continuously train medical data regulations because it contributes for the work of correct implementation and compliance with standards in healthcare 'at the stage of development of the current period of AI application' (Shaw et al., 2018).\\u003c/p\\u003e \\u003cp\\u003eProper regulatory framework must then be achieved to support the new healthcare concepts as healthcare quality protection and patient rights defence including. It necessitated established guidelines and sharp continuous training to support the development of a new technology (McMahon et al., 2020). To use recently available tools that improve complex information management without sacrificing patient security and confidentiality, healthcare workers need an implementation of ethical data handling practises.\\u003c/p\\u003e \\u003cp\\u003eAI holds significant promise for supporting healthcare, as noted by Car et al. (\\u003cspan citationid=\\\"CR12\\\" class=\\\"CitationRef\\\"\\u003e2019\\u003c/span\\u003e) and Fatima et al. (\\u003cspan citationid=\\\"CR17\\\" class=\\\"CitationRef\\\"\\u003e2020\\u003c/span\\u003e). However, they caution against ungrounded enthusiasm regarding AI\\u0026rsquo;s applications, emphasizing the necessity of data privacy and responsible AI deployment. Skilled management solutions are essential for streamlining data and codes. Alami et al. (\\u003cspan citationid=\\\"CR5\\\" class=\\\"CitationRef\\\"\\u003e2020\\u003c/span\\u003e) and Ahmad et al. (\\u003cspan citationid=\\\"CR4\\\" class=\\\"CitationRef\\\"\\u003e2022\\u003c/span\\u003e) examine both the positive and negative impacts of AI on health disparities in underdeveloped countries. Their findings highlight various benefits, such as improving access to medical services and enhancing disease forecasting techniques. Nonetheless, Gwagwa et al. (\\u003cspan citationid=\\\"CR26\\\" class=\\\"CitationRef\\\"\\u003e2021\\u003c/span\\u003e) identify challenges like data bias issues and management complexities, advocating for a prudent approach.\\u003c/p\\u003e \\u003cp\\u003eAccording to Alami et al. (\\u003cspan citationid=\\\"CR5\\\" class=\\\"CitationRef\\\"\\u003e2020\\u003c/span\\u003e), successfully deployed digital health instruments in Africa with artificial intelligence are powerful forces behind health system evolution in developing countries. Because of this need for dedicated support infrastructure in addition to regulatory standards with consistent funding commitments to sustain AI healthcare solutions, a thorough approach is mandatory that goes beyond simple technology implementation (Kerasidou, \\u003cspan citationid=\\\"CR34\\\" class=\\\"CitationRef\\\"\\u003e2021\\u003c/span\\u003e). This research gives the reason for why data fairness issues need to be addressed firstly, while building high quality healthcare AI applications.\\u003c/p\\u003e \\u003cp\\u003eCar et al. (\\u003cspan citationid=\\\"CR12\\\" class=\\\"CitationRef\\\"\\u003e2019\\u003c/span\\u003e) suggest that big data and AI in health care can be affordable and useful, but not enough evidence is out there that AI products would bring considerable benefits to the health care. The researchers say that the results are so exact they depend on exact results and make concrete differences for medical practise. Research was conducted on ethical issues emphasizing health care requirements for better rules and privacy security as well as responsible AI practises (Brand et al., \\u003cspan citationid=\\\"CR10\\\" class=\\\"CitationRef\\\"\\u003e2022\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003eFatima et al. (\\u003cspan citationid=\\\"CR17\\\" class=\\\"CitationRef\\\"\\u003e2020\\u003c/span\\u003e) studied national AI plans to analyse their impact on public sector improvement and business competition systems. Data and computer programme management under the public sector plays a vital role as the study demonstrates the fundamental importance of governance in this field. National governments require substantial investments that target AI capability development most especially in building new capabilities. The research advocated for tactical steps to improve AI exploitation capacity since AI represents a fundamental requirement for national advancements in the future (Effoduh et al., \\u003cspan citationid=\\\"CR16\\\" class=\\\"CitationRef\\\"\\u003e2023\\u003c/span\\u003e; Reddy et al., \\u003cspan citationid=\\\"CR39\\\" class=\\\"CitationRef\\\"\\u003e2019\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003eThe studies by Car et al. (\\u003cspan citationid=\\\"CR12\\\" class=\\\"CitationRef\\\"\\u003e2019\\u003c/span\\u003e) and Fatima et al. (\\u003cspan citationid=\\\"CR17\\\" class=\\\"CitationRef\\\"\\u003e2020\\u003c/span\\u003e) presented opposing opinions though Zuhair et al. (\\u003cspan citationid=\\\"CR54\\\" class=\\\"CitationRef\\\"\\u003e2024\\u003c/span\\u003e) established that AI deployment in health diagnosis, prognosis prediction and patient care management alongside hospital and community healthcare services leads to better medical system efficiency specifically in densely populated health facilities with insufficient resources at developing world locations.\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec13\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e3.4. Ethical Considerations in AI and Healthcare\\u003c/h2\\u003e \\u003cp\\u003eFosso \\u0026amp; Queiroz (\\u003cspan citationid=\\\"CR19\\\" class=\\\"CitationRef\\\"\\u003e2023\\u003c/span\\u003e) investigated the connexion between AI and digital health utilising bibliometrics as their research method. The research analysed how responsible AI works while investigating moral and ethical problems that exist in healthcare AI systems. By employing a systematic technique, the authors found the changes together with challenges and positive outcomes of AI integration in healthcare within World Health Organisation ethical framework for AI (WHO 2021). This document delivered detailed instructions along with practical advice for medical practitioners who follow the main contemplation of ethics alongside regulation when using AI to transform healthcare. These studies (\\u003cspan refid=\\\"Sec19\\\" class=\\\"InternalRef\\\"\\u003eAppendix 1\\u003c/span\\u003e) demonstrated the need for fair medical service delivery which includes moral considerations with rule compliance alongside essential infrastructure creation. AI healthcare utilisation develops toward responsible behaviour and inclusion through cohesive element integration to secure benefits for numerous people.\\u003c/p\\u003e \\u003c/div\\u003e\"},{\"header\":\"4. Discussion\",\"content\":\"\\u003cdiv id=\\\"Sec15\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e4.1 Key Findings and their Comparison with Existing Evidence\\u003c/h2\\u003e \\u003cp\\u003eThis review identified three key findings across 41 studies:\\u003c/p\\u003e \\u003cp\\u003e(1) A dominant use of AI in diagnostic and predictive functions,\\u003c/p\\u003e \\u003cp\\u003e(2) Limited application of AI in health systems management and non-communicable diseases, and\\u003c/p\\u003e \\u003cp\\u003e(3) Underrepresentation of robust data governance structures and ethical frameworks in most implementations.\\u003c/p\\u003e \\u003cp\\u003eFirst, our analysis revealed a strong orientation toward AI tools used for disease diagnosis and prediction, particularly for infectious diseases like tuberculosis, malaria, and HIV. This was expected due to the high disease burden in Sub-Saharan Africa. However, the narrow thematic focus, despite widespread calls for digital health innovation, suggests limited diversification of AI applications in African contexts. This finding aligns with Wahl et al. (\\u003cspan citationid=\\\"CR50\\\" class=\\\"CitationRef\\\"\\u003e2018\\u003c/span\\u003e), who found similar trends in global LMICs but contrasts with recent high-income country studies showing broader AI usage in surgical planning and chronic disease management (Singh et al., \\u003cspan citationid=\\\"CR44\\\" class=\\\"CitationRef\\\"\\u003e2023\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003eSecond, we observed a significant gap in the deployment of AI systems in areas such as hospital management, patient scheduling, and healthcare logistics. Only 7 of the 41 studies addressed health system optimization. This indicates that while technical AI innovation is progressing, its integration into administrative layers remains minimal. This partially disaffirms earlier projections (e.g., Kamruzzaman, \\u003cspan citationid=\\\"CR32\\\" class=\\\"CitationRef\\\"\\u003e2021\\u003c/span\\u003e) that foresaw rapid AI expansion into these domains across LMICs.\\u003c/p\\u003e \\u003cp\\u003eThird, few studies (n\\u0026thinsp;=\\u0026thinsp;6) integrated structured data governance frameworks or evaluated ethical implications systematically. This is concerning, considering the centrality of data privacy, algorithmic bias, and informed consent to the sustainability of AI in healthcare. Although some studies (e.g., Ademolowo et al., \\u003cspan citationid=\\\"CR2\\\" class=\\\"CitationRef\\\"\\u003e2023\\u003c/span\\u003e) advocate for national-level policy formulation, practical enforcement and ethical implementation remain limited. Our findings support Gwagwa et al. (\\u003cspan citationid=\\\"CR26\\\" class=\\\"CitationRef\\\"\\u003e2021\\u003c/span\\u003e), who emphasized the embryonic state of ethical AI governance in Africa, especially at clinical points of care.\\u003c/p\\u003e \\u003cp\\u003eFinally, a cross-regional imbalance was observed. Countries like South Africa and Kenya dominate scholarly output, while large portions of Central and North Africa remain underrepresented. This affirms previous geographic analyses by Alami et al. (\\u003cspan citationid=\\\"CR5\\\" class=\\\"CitationRef\\\"\\u003e2020\\u003c/span\\u003e), who noted a concentration of AI experimentation in relatively better-resourced countries.\\u003c/p\\u003e \\u003cp\\u003eFigure \\u003cspan refid=\\\"Fig4\\\" class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\u003e illustrates the case study on the integration of AI technology into tuberculosis detection in Kenya, which highlights a three-phase implementation process, namely, deployment of the pilot program, training of healthcare workers and integration into national health databases. Despite challenges, such as the limitations and costs of infrastructure, the initiative enhanced diagnostic accuracy and capacity building and was supported by diverse funding sources, including government, international aid and public\\u0026ndash;private partnerships.\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003cp\\u003eAccording to Car et al.'s (\\u003cspan citationid=\\\"CR12\\\" class=\\\"CitationRef\\\"\\u003e2019\\u003c/span\\u003e) research they investigated big data and AI convergence applications in healthcare through an analysis of modern data processing systems which became accessible and affordable. This research proved that there are insufficient empirical data showing how AI products improve medical care quality. The study demonstrates a need for ethical decision practices that deal with data sharing problems and privacy protection through valuable laws controlling AI healthcare administration. Fatima et al. (\\u003cspan citationid=\\\"CR17\\\" class=\\\"CitationRef\\\"\\u003e2020\\u003c/span\\u003e) examined American AI strategic plans in order to show how governance establishes productive public services and advantageous market competition. The comprehensive plans need specific regulations to build proper data handling systems and determine research methods as described by the authors. AI technologies need appropriate control systems embedded in national strategy documents for managing effective resources and optimizing their use according to Jogi (\\u003cspan citationid=\\\"CR30\\\" class=\\\"CitationRef\\\"\\u003e2021\\u003c/span\\u003e). Mahomed (2018) advises nations to dedicate generous resources for developing their AI capabilities on a national level. A country requires financial investment in AI development because the enhancement enables better application of AI in future development initiatives and competitive strategies.\\u003c/p\\u003e \\u003cp\\u003eSeveral research findings confirm that combined approaches of digital health technologies and AI possess solid potential to transform medical services throughout LMICs. The authors advocate for nationwide strategic AI healthcare plans supported by strong oversight systems which require continuous financial support to achieve successful execution. AI's transformative capabilities require recognition at the same time as the critical need exists to solve problems associated with biased data points and ethical frameworks as well as complex regulatory systems. These investigations show that a cohesive approach should advance beyond standard technological unification through ethical rulemaking and proper implementation systems for inclusive healthcare services.\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec16\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e4.2 Limitations of the Study\\u003c/h2\\u003e \\u003cp\\u003eThis study is not without limitations. First, it only included peer-reviewed articles in English, potentially excluding valuable grey literature or regional publications in French, Arabic, or Portuguese-speaking African countries. Second, despite using multiple databases, publication bias may have influenced the visibility of certain studies\\u0026mdash;especially those from countries with limited research infrastructure. Subjective judgments about themes emerged during thematic interpretation because the PRISMA framework enhances methodological transparency yet does not eliminate differences between reviewers. Recent technological developments in AI surpass what can be found in current academic publications.\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec17\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e4.2. Challenges and Future Directions\\u003c/h2\\u003e \\u003cp\\u003eThe AI delivers a transformative potential, but challenges such as lacking localised data, lower digital literacy, infrastructural inequality, and underdeveloped regularity frameworks persist. Overcoming these requires multi-level investment in education, infrastructure, and regulatory capacity. Future research should emphasise longitudinal impacts of AI integration on patient outcomes, comparative analysis across African subregions, and development of localized ethical and regulatory standards.\\u003c/p\\u003e \\u003c/div\\u003e\"},{\"header\":\"5. Conclusions\",\"content\":\"\\u003cp\\u003eIntegrating AI into healthcare, particularly in LMICs, offers substantial opportunities but presents formidable challenges. Research highlighted the transformative potential of AI in enhancing disease detection, diagnosis and healthcare accessibility in resource-constrained settings. The selected studies underscored the crucial role of comprehensive national strategies, robust governance systems and substantial investments in realizing the full potential of AI while addressing ethical concerns and biases.\\u003c/p\\u003e \\u003cp\\u003eMultiple hurdles present during the implementation of AI technology in healthcare operations need to be addressed. A detailed approach must be used to resolve privacy and fairness issues and digital inequality matters. Research activity with team-based collaboration and mutable strategies must remain essential for handling healthcare problems to ensure AI delivers benefits across every healthcare setting equitably.\\u003c/p\\u003e \\u003cp\\u003eEvery background should learn about AI computing and gain essential proficiencies to access AI tools through all resource channels the meetings emphasize. The partnership agenda connects healthcare access between different communities to generate superior health outcomes for vulnerable population groups. The future of AI in healthcare depends on its careful execution through patient trust programs that combine moral principles for maximal technological benefits to both health institutions and global health management. Active continuous work in AI healthcare integration depends on combined operating efforts to produce technological developments which benefit all patients in standardized healthcare environments.\\u003c/p\\u003e\"},{\"header\":\"Declarations\",\"content\":\"\\u003ch2\\u003eFunding:\\u003c/h2\\u003e \\u003cp\\u003eWellcome Trust through Warwick University and the University of Nairobi.\\u003c/p\\u003e\\u003ch2\\u003eAcknowledgements\\u003c/h2\\u003e \\u003cp\\u003eProf. Sekalala, Sharifah \\u0026ndash; Warwick University\\u003c/p\\u003e \\u003cp\\u003eDr. Ben Mkalama \\u0026ndash; University of Nairobi\\u003c/p\\u003e \\u003cp\\u003eYureshya.Y.S.Perera \\u0026ndash; Warwick University\\u003c/p\\u003e \\u003cp\\u003eAl Kags and the entire team at Open Institute\\u003c/p\\u003e\"},{\"header\":\"References\",\"content\":\"\\u003col\\u003e\\n\\u003cli\\u003eAdebamowo CA, Callier S, Akintola S, Maduka O, Jegede A, Arima C, Ogundiran T, Adebamowo SN, BridgELSI Project as part of the DS-I Africa Consortium. 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J Am Med Inform Assoc. 2020;27:491\\u0026ndash;7. https://doi.org/10.1093/jamia/ocz192\\u003c/li\\u003e\\n\\u003cli\\u003eSchwalbe N, Wahl B. Artificial intelligence and the future of global health. Lancet. 2020;395:1579\\u0026ndash;86. https://doi.org/10.1016/S0140-6736(20)30226-9\\u003c/li\\u003e\\n\\u003cli\\u003eSeto E, Leonard KJ, Cafazzo JA, Barnsley J, Masino C, Ross HJ. Developing healthcare rule-based expert systems: case study of a heart failure telemonitoring system. Int J Med Inform. 2012;81:556\\u0026ndash;65. https://doi.org/10.1016/j.ijmedinf.2012.03.001\\u003c/li\\u003e\\n\\u003cli\\u003eShaw J, Rudzicz F, Jamieson T, Goldfarb A. Artificial intelligence and the implementation challenges. J Med Internet Res. 2019;21:e13659. https://doi.org/10.2196/13659.\\u003c/li\\u003e\\n\\u003cli\\u003eSingh A, Madaan G, Swapna HR, Kumar A. Impact of artificial intelligence on human capital in healthcare sector post-Covid-19. In: The adoption and effect of artificial intelligence on human resources management, part A. Leeds: Emerald Publishing Limited; 2023. pp. 47\\u0026ndash;69.\\u003c/li\\u003e\\n\\u003cli\\u003eSingh A, Madaan G, Swapna HR, Kumar A. Impact of artificial intelligence on human capital in healthcare sector post-Covid-19. In: The adoption and effect of artificial intelligence on human resources management, Part A. Leeds: Emerald Publishing Limited; 2023. p. 47\\u0026ndash;69.\\u003c/li\\u003e\\n\\u003cli\\u003eStaunton C, Adams R, Horn L, Labuschaigne M. A framework to govern the use of health data for research in Africa: a South African perspective. In: Zima T, Weisstub DN, editors. Medical research ethics: challenges in the 21st Century. Berlin: Springer International Publishing; 2023. pp. 485\\u0026ndash;99.\\u003c/li\\u003e\\n\\u003cli\\u003eSun TQ, Medaglia R. Mapping the challenges of artificial intelligence in the public sector: evidence from public healthcare. Gov Inf Q. 2019;36:368\\u0026ndash;83. https://doi.org/10.1016/j.giq.2018.09.008\\u003c/li\\u003e\\n\\u003cli\\u003eTaeihagh A. Governance of artificial intelligence. Policy Soc. 2021;40:137\\u0026ndash;57. https://doi.org/10.1080/14494035.2021.1928377\\u003c/li\\u003e\\n\\u003cli\\u003eTanui CK, Ndembi N, Kebede Y, Tessema SK. Artificial intelligence to transform public health in Africa. Lancet Infect Dis. 2024;24:e542. https://doi.org/10.1016/S1473-3099(24)00435-3\\u003c/li\\u003e\\n\\u003cli\\u003eWahl B, Cossy-Gantner A, Germann S, Schwalbe NR. Artificial intelligence (AI) and global health: how can AI contribute to health in resource-poor settings? BMJ Glob Health. 2018;3:e000798. https://doi.org/10.1136/bmjgh-2018-000798\\u003c/li\\u003e\\n\\u003cli\\u003eWaljee AK, Weinheimer-Haus EM, Abubakar A, Ngugi AK, Siwo GH, Kwakye G, Singal AG, Rao A, Saini SD, Read AJ, Baker JA, Balis U, Opio CK, Zhu J, Saleh MN. Artificial intelligence and machine learning for early detection and diagnosis of colorectal cancer in sub-Saharan Africa. Gut. 2022;71:1259\\u0026ndash;65. https://doi.org/10.1136/gutjnl-2022-327211\\u003c/li\\u003e\\n\\u003cli\\u003eWinter JS, Davidson E. Big data governance of personal health information and challenges to contextual integrity. Inf Soc. 2019;35:36\\u0026ndash;51. https://doi.org/10.1080/01972243.2018.1542648\\u003c/li\\u003e\\n\\u003cli\\u003eWorld Health Organization. Ethics and governance of artificial intelligence for health: WHO guidance. Geneva: World Health Organization; 2021. https://www.who.int/publications/i/item/9789240029200\\u003c/li\\u003e\\n\\u003cli\\u003eZuhair V, Babar A, Ali R, Oduoye MO, Noor Z, Chris K, Okon II, Rehman LU. Exploring the impact of artificial intelligence on global health and enhancing healthcare in developing nations. J Prim Care Community Health. 2024;15:21501319241245847. https://doi.org/10.1177/21501319241245847\\u003c/li\\u003e\\n\\u003cli\\u003eZuiderwijk A, Chen YC, Salem F. Implications of the use of artificial intelligence in public governance: A systematic literature review and a research agenda. Gov Inf Q. 2021;38:101577. https://doi.org/10.1016/j.giq.2021.101577. \\u003c/li\\u003e\\n\\u003c/ol\\u003e \"}],\"fulltextSource\":\"\",\"fullText\":\"\",\"funders\":[{\"identity\":\"b79a9ff3-f2ef-4494-ada8-c462ce56560f\",\"identifier\":\"10.13039/100010269\",\"name\":\"Wellcome Trust\",\"awardNumber\":\"Grant REF: 222429/2/1/2\",\"order_by\":0}],\"hasAdminPriorityOnWorkflow\":false,\"hasManuscriptDocX\":true,\"hasOptedInToPreprint\":true,\"hasPassedJournalQc\":\"\",\"hasAnyPriority\":true,\"hideJournal\":true,\"highlight\":\"\",\"institution\":\"University of Warwick\",\"isAcceptedByJournal\":false,\"isAuthorSuppliedPdf\":false,\"isDeskRejected\":\"\",\"isHiddenFromSearch\":false,\"isInQc\":false,\"isInWorkflow\":false,\"isPdf\":false,\"isPdfUpToDate\":true,\"isWithdrawnOrRetracted\":false,\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"researchsquare\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":true,\"externalIdentity\":\"\",\"sideBox\":\"\",\"snPcode\":\"\",\"submissionUrl\":\"/submission\",\"title\":\"Research Square\",\"twitterHandle\":\"researchsquare\",\"acdcEnabled\":true,\"dfaEnabled\":false,\"editorialSystem\":\"\",\"reportingPortfolio\":\"\",\"inReviewEnabled\":false,\"inReviewRevisionsEnabled\":true},\"keywords\":\"Artificial Intelligence, Data Governance, Healthcare Systems, Ethics, Health Policy, Africa Artificial Intelligence, Healthcare, Ethics, Data Governance, Health Policy, Africa\",\"lastPublishedDoi\":\"10.21203/rs.3.rs-6572611/v1\",\"lastPublishedDoiUrl\":\"https://doi.org/10.21203/rs.3.rs-6572611/v1\",\"license\":{\"name\":\"CC BY 4.0\",\"url\":\"https://creativecommons.org/licenses/by/4.0/\"},\"manuscriptAbstract\":\"\\u003cp\\u003eBackground: African health care Systems are confronted by continuing infrastructure disparities or lack of capacity about the workforce and unequal access to care. To address these challenges, artificial intelligence (AI) and data governance frameworks have become the change makers in the health sector, especially in the wake of the African Union Convention on Cyber Security and Personal Data Protection (2014) which was used to provide the foundation upon which digital innovators look to bring solutions to life in the healthcare sector.\\u003c/p\\u003e\\n\\u003cp\\u003ePurpose: This study investigates the integration of AI in African healthcare systems post-2014. It examines the role of AI in disease detection, patient management and other health outcome, ethical standards and policy development implications.\\u003c/p\\u003e\\n\\u003cp\\u003eMethods: A total of 41 peer-reviewed articles published in the years between 2015 and 2023 were analysed by means of a systematic review of the literature. PubMed, IEEE Xplore, and Google Scholar were used as the sources. Search terms included “AI in healthcare,” “data governance” and “Africa,”. Also, the review has included the grey literature which did not follow the PRISMA guidelines. Empirical evidence of implementation of AI across African countries in the healthcare contexts was the basis of the studies selected.\\u003c/p\\u003e\\n\\u003cp\\u003eResults: The number of publications related to healthcare via AI has increased by 35% in 2022-2023 colocation with increasing interest and investment in this issue. It was found out that AI: contributes to significant parts of diagnostic accuracy, operational efficiency and predictive analytics in several medical disciplines. Despite these critical challenges, there remain two major sticking points regarding infrastructure, regulatory enforcement, and usage of patient data in the ethical way.\\u003c/p\\u003e\\n\\u003cp\\u003eConclusions: The study indicates the need for the immediate development of targeted interventions by the African governments, health ministries, research institutions, and digital health stakeholders. With these include the establishment of robust governance mechanism, ethical oversight frameworks and digital infrastructure as investment. In the future, it is suggested that longitudinal study and cross regional policy comparison should be conducted to provide sufficient support for sustainability of AI integration in healthcare systems.\\u003c/p\\u003e\",\"manuscriptTitle\":\"Revolutionizing African Healthcare: A Systematic Review of Artificial Intelligence and Data Governance\",\"msid\":\"\",\"msnumber\":\"\",\"nonDraftVersions\":[{\"code\":1,\"date\":\"2025-05-05 09:46:02\",\"doi\":\"10.21203/rs.3.rs-6572611/v1\",\"editorialEvents\":[{\"type\":\"communityComments\",\"content\":0}],\"status\":\"published\",\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"researchsquare\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":true,\"externalIdentity\":\"\",\"sideBox\":\"\",\"snPcode\":\"\",\"submissionUrl\":\"/submission\",\"title\":\"Research Square\",\"twitterHandle\":\"researchsquare\",\"acdcEnabled\":true,\"dfaEnabled\":false,\"editorialSystem\":\"\",\"reportingPortfolio\":\"\",\"inReviewEnabled\":false,\"inReviewRevisionsEnabled\":true}}],\"origin\":\"\",\"ownerIdentity\":\"617a0393-6857-4735-a080-aea409764d13\",\"owner\":[],\"postedDate\":\"May 5th, 2025\",\"published\":true,\"recentEditorialEvents\":[],\"rejectedJournal\":[],\"revision\":\"\",\"amendment\":\"\",\"status\":\"posted\",\"subjectAreas\":[{\"id\":47953207,\"name\":\"Agricultural Economics \\u0026 Policy\"}],\"tags\":[],\"updatedAt\":\"2025-05-05T09:46:02+00:00\",\"versionOfRecord\":[],\"versionCreatedAt\":\"2025-05-05 09:46:02\",\"video\":\"\",\"vorDoi\":\"\",\"vorDoiUrl\":\"\",\"workflowStages\":[]},\"version\":\"v1\",\"identity\":\"rs-6572611\",\"journalConfig\":\"researchsquare\"},\"__N_SSP\":true},\"page\":\"/article/[identity]/[[...version]]\",\"query\":{\"redirect\":\"/article/rs-6572611\",\"identity\":\"rs-6572611\",\"version\":[\"v1\"]},\"buildId\":\"8U1c8b4HqxoKbykW_rLl7\",\"isFallback\":false,\"isExperimentalCompile\":false,\"dynamicIds\":[84888],\"gssp\":true,\"scriptLoader\":[]}","source_license":"CC-BY-4.0","license_restricted":false}