The relentless advancement of Artificial Intelligence (AI) and machine learning technologies heralds a transformative epoch for global public health systems. These tools offer unprecedented capabilities for data synthesis, pattern recognition, and predictive analytics, thereby presenting a paradigm shift in how societies monitor, understand, and respond to health threats. This potential is particularly salient for the African continent, which bears a disproportionate burden of infectious disease morbidity and mortality amidst systemic challenges related to infrastructure, financing, and human resources. The integration of AI into public health surveillance and communication frameworks is not merely an incremental improvement but a potential catalyst for a fundamental leap in health equity and crisis responsiveness ( Topol, 2019 ; Myers et al., 2021 ).

The urgency for such innovation is underscored by Africa’s complex and dynamic health landscape. The continent remains persistently vulnerable to outbreaks of established pathogens such as cholera, tuberculosis, malaria, and HIV/ AIDS, while simultaneously confronting emerging and re-emerging threats, including COVID-19, mpox, and Zika virus ( Nkengasong & Tessema, 2020 ). These challenges are exacerbated by structural frailties, including fragmented health information systems, a critical shortage of skilled healthcare labor, and logistical constraints in rural and remote areas. For example, Africa CDC (2024) reports ramping up SARS-CoV-2 genomics and bioinformatics training to strengthen local surveillance capacity. Furthermore, the accelerating crisis of climate change introduces new epidemiological uncertainties, altering the geographic ranges of vector-borne diseases and increasing the frequency of climate-sensitive health events, thereby demanding more agile and predictive surveillance modalities ( Rocklöv & Dubrow, 2020 ; Tshimula et al., 2024 ). In this context, AI-driven solutions ranging from predictive modeling of outbreak trajectories to natural language processing (NLP) for syndromic surveillance offer a compelling strategy to augment overstretched public health infrastructures ( Tanui et al., 2024 ; El Morr et al., 2024 ; Villanueva-Miranda et al., 2025 ).

Notwithstanding its global proliferation, the operational integration of AI within African public health ecosystems remains nascent and markedly heterogeneous. While nations such as South Africa, Rwanda, and Ghana demonstrated the utility of AI-powered chatbots for disseminating vetted health information and countering misinformation during the COVID-19 pandemic, these initiatives often represented isolated triumphs rather than systemic integration ( Ndembi et al., 2025 ). The Nigerian experience, as detailed by Ezeaka (2024) , is emblematic of the broader continental impediments, which include a pervasive lack of standardized data infrastructure, critically limited data literacy among health professionals, profound concerns regarding data privacy and ethical compliance, and a deep digital divide that excludes marginalized populations. Consequently, the benefits of AI are risk being accrued only to technologically resourced urban centers, thereby potentially exacerbating existing health inequities rather than ameliorating them ( Wahl et al., 2023 ).

A critical examination of the current landscape reveals two further, profound challenges. First, a significant proportion of AI applications fail to progress beyond the pilot phase or are deployed without rigorous evaluation for tangible health outcomes, a phenomenon often termed ‘pilotitis’ ( Egermark et al., 2022 ). WHO’s own reporting reinforces this concern: digital health programmes frequently lack monitoring or evaluation frameworks even at regional levels ( WHO Regional Office for Europe, 2023 ). Second, there exists a conspicuous deficit of culturally and linguistically adapted tools. For instance, while NLP systems have demonstrated efficacy in improving health communication and vaccine acceptance in high-income contexts ( Akpatsa et al., 2022 ; Cascini et al., 2022 ; Perikli et al., 2023 ), their application in Africa is limited by a failure to integrate low-resource local languages and dialectal variations, such as Yoruba or Zulu, which are critical for effective community engagement ( Hu et al., 2025 ; Adelani, 2025 ; Njoga et al., 2022 ; Sadiq et al., 2023 ). Moreover, the pursuit of algorithmic fairness and transparency is often pursued through a techno-centric lens ( TGov Team, 2024 ; World Health Organization, 2023 ; World Health Organization Regional Office for Europe, 2023 ), with only a minority of initiatives developing frameworks that consciously address deeply contextual factors such as colonial legacies, local power structures, community sentiment, and mechanisms for legal and ethical accountability ( Ndembi et al., 2025 ; Abebe et al., 2020 ).

This paper, therefore, seeks to provide a comprehensive and critical analysis of the integration of AI within Africa’s public health communication and surveillance apparatus. It moves beyond a mere cataloguing of applications to interrogate the conceptual, ethical, and practical gaps that hinder sustainable and equitable implementation. In capsule words, the objectives of the study are to: (1) assess evidence of AI applications, and successes in Africa, (2) barriers, and gaps. Therefore, this study raises the following questions: (1) What are the key evidence of AI applications in Africa and its successes? (2) What barriers that hinder scalability and apparent theoretical gaps? By proffering answer to these objectives and questions, this study is of essence as it will inform future policy formulation, advance contextually grounded ethical considerations, and propose culturally relevant innovation strategies that are essential for optimizing AI’s transformative potential in strengthening Africa’s public health resilience.

There is a conceptual gap in the discussion of artificial intelligence in Africa. There is a lack of a theoretical framework that guides the ethical use of artificial intelligence. Although the existing studies have vastly discussed the benefits of using AI in public health, not many of them have tried to anchor these innovative technologies within theoretical models. Unfortunately, this affects the ability to examine how AI can be designed and used in ways that consider the local realities, especially in rural areas with low digital literacy and inadequate infrastructure. This gap affects the development of guiding principles as Asiedu et al. (2024) argue that AI initiatives must address colonial legacies by “globalizing fairness,” ensuring that local priorities and ethical values guide technology design that could foster trust and fairness in the adoption of artificial intelligence in public health. Kondo et al. (2023) also note that AI and healthcare research in Africa is still nascent and concentrated in a few regions, underscoring the need to broaden and diversify the field. It is also worth noting the decolonization of technological advancement. This calls for focusing implementation of AI that considers the local ownership, cultural resonance, and relevance. Many of the AI technologies now are designed, governed, and funded by organizations outside Africa. This results in sidelining the local system and causes technological dependence. As such, AI technology rooted in local languages, narratives, and practices needs to be developed in Africa.

This study adopted a rigorous qualitative evidence synthesis methodology to critically interrogate the integration of artificial intelligence within Africa’s public health communication apparatus. A systematic and replicable search strategy was employed to identify relevant peer-reviewed literature and organizational reports published between 2013 and 2024. The inclusion criteria were deliberately circumscribed to materials focusing on empirical applications of AI in public health communication within the African context, ensuring both contextual specificity and analytical depth. To mitigate publication bias and incorporate policy-relevant insights, a significant body of grey literature from entities such as the World Health Organization and the Africa CDC was also curated.

The initial search yielded over 180 materials, which were subsequently subjected to a multi-stage screening process. Irrelevant and duplicate records were excluded, resulting in a final corpus of 41 documents for in-depth critical review. The analytical approach was guided by thematic analysis, an inductive methodology well-suited for synthesising qualitative evidence across a diverse set of sources. Key themes, including linguistic integration, ethical frameworks, and infrastructural constraints, were identified and developed through a process of iterative coding and constant comparison. The synthesis itself was conceptual and critical in nature, moving beyond mere description to construct a nuanced analysis of emerging evidence, documented successes, and persistent implementation gaps. This was achieved through comparative insight and the use of case examples, thereby illuminating the complex interplay between technological potential and contextual reality that defines the current state of AI adoption in African public health. These criteria ensured that the paper has a diverse perspective, specifically focusing on a particular context. To achieve comprehensiveness, more materials were extracted from gray literature, such as reports from reputable organizations (World Health Organization, Ministries of Health, and the African Union), conference reports. This approach aligns with Greenhalgh et al. (2018) , who argue that inductive, narrative syntheses are valuable for integrating diverse qualitative evidence when formal systematic methods are impractical.

Verified studies analysis table for application of ai in public health in Africa.

The integration of Artificial Intelligence (AI) into public health systems represents a paradigm shift with the potential to redefine disease surveillance, health communication, and service delivery. Nowhere is this potential more tantalising, or its realisation more fraught with complexity, than across the diverse and dynamic continent of Africa. This discussion synthesises evidence from a corpus of studies, detailed in the above table, to critically assess the application and documented successes of AI technologies in strengthening Africa’s public health infrastructure. It subsequently conducts a rigorous examination of the persistent barriers and implementation gaps that threaten to stifle this potential, creating a chasm between technological promise and tangible impact. The analysis reveals that while AI offers transformative tools for outbreak response, health communication, and clinical support, its effective adoption is critically dependent on overcoming foundational challenges in infrastructure, data governance, and local capacity building.

I. Evidence of AI Applications and Documented Successes

The evidence collated demonstrates that AI applications in Africa are not merely theoretical but are being actively deployed across a spectrum of public health domains, with several studies reporting measurable successes.

a) Outbreak Surveillance and Early Warning Systems

A significant concentration of AI application is evident in the domain of epidemic preparedness and response, largely catalysed by the 2014-2016 West Africa Ebola outbreak. Studies by Joshi et al. (2020) and Odlum & Yoon (2015) exemplify the use of Natural Language Processing (NLP) and machine learning to mine social media data (specifically Twitter) for early signals of disease activity. Indeed, journalistic accounts of the 2014 Ebola crisis note that AI-driven systems (such as HealthMap) detected outbreak signals before official reports, showcasing AI’s promise in early detection ( Scherer, 2014 ). Joshi et al. (2020) demonstrated that an automated system could detect signals of an Ebola outbreak before official reports were released, showcasing AI’s potential for radical improvements in early warning timelines. Similarly, Odlum & Yoon (2015) and Lazard et al. (2015) utilised NLP for real-time ‘public sentiment analysis’ and thematic tracking during health crises. Their work proved that AI could effectively map public concerns, misinformation pathways, and overall sentiment, providing health authorities with a crucial tool for crafting targeted, responsive communication campaigns ( Odlum & Yoon, 2015 ; Lazard et al., 2015 ).

Beyond digital chatter, AI is being applied to genomic data for pathogen surveillance ( African Centre of Excellence for Genomics of Infectious Diseases, 2025 ). Initiatives like the Pathogen Genomics Initiative ( Makoni, 2020 ) and the SENTINEL system ( Botti-Lodovico et al., 2021 ) represent a sophisticated convergence of AI and genomics. The Broad Institute (2024) reports deploying a new viral surveillance system in West Africa to help prevent the next pandemic, illustrating investment in genomic AI tools. These systems are designed to provide ‘early-warning for pandemics’ and enhance ‘outbreak attribution’ by tracking viral evolution and spread. The successful establishment of a ‘functional early-warning system’ as noted by Botti-Lodovico et al. (2021) , marks a monumental leap in Africa’s capacity to identify and respond to viral threats from a position of knowledge rather than reaction.

b) Health Communication and Information Dissemination

The role of AI in managing the complex information ecosystem of public health is another area of prolific activity ( World Health Organization, 2018 ). However, the evidence here is dichotomous, highlighting both the power and the peril of digital platforms. Studies analysing broad platforms like YouTube ( Pathak et al., 2015 ; Basch et al., 2015 ) revealed their significant role as sources of health information during the Ebola crisis ( Gidado et al., 2015 ). However, they also uncovered a ‘high proportion of incomplete/misleading information’ and ‘variable quality and accuracy’, underscoring a major challenge that AI itself must help solve ( Pathak et al., 2015 ; Basch et al., 2015 ).

In response, AI-driven chatbots are emerging as a promising tool for delivering accurate, accessible health information. A good case of this is the Rwanda’s official ‘RBC-Mbaza’ COVID-19 chatbot that reached over 580,000 users (~15,000 per day) by delivering localized, up-to-date information via simple mobile text in local languages ( European Commission, 2022 ). The scoping review by Phiri et al. (2023) documents a ‘growing field’ of health chatbots across Africa, applied in triage, patient education, and treatment adherence. More robust evidence comes from Kleinau et al. (2024) , whose randomised controlled trial in Malawi provided clear ‘evidence of success & impact’ by demonstrating that a mental health chatbot effectively improved the wellbeing of health workers. Similar national level chatbot deployments in Malawi also show wide reach and adaptability ( Ndemera et al., 2025 ). This study is particularly notable for its rigorous methodology, moving beyond conceptual promise to empirical validation. Similarly, research into chatbots for sexual and reproductive health (SRH) in Kenya and Zambia shows preliminary evidence of ‘acceptability and potential effectiveness’ ( Mills et al., 2023 ; Njogu et al., 2023 ; Yam et al., 2022 ; McMahon et al., 2023 ). And, in Nigeria, the AwaDoc platform uses an AI-driven WhatsApp chatbot to provide personalized medical advice 24/7, making health information widely accessible to users ( Abdulrahman, 2025 ; MedTechPulse, 2025 ; Clafiya, 2025 ). Moreover, CARE Nigeria (2024) similarly reports using WhatsApp to conduct community immunization awareness campaigns, exemplifying how such common platforms are leveraged for public health outreach.

c) Operational Efficiency and Healthcare Delivery

AI’s value extends beyond information to directly optimising healthcare processes. Fung et al. (2016) , in their systematic review, recognised the utility of social media analytics for ‘outbreak surveillance’ and tracking ‘epidemic activity’. Masresha et al. (2020) found that even low-tech tools like WhatsApp dramatically improved immunization campaign coordination in Nigeria, supporting Gavi’s (2024) observation that frontline health workers are deploying ordinary apps to make an extraordinary difference. On a more logistical level, Trad et al. (2015) proposed an SMS-based system to guide patients to suitable health facilities, a concept aimed at improving triage and resource allocation during a crisis. Furthermore, Nair et al. (2022) explored the use of predictive analytics and machine learning for ‘optimizing vaccination interventions’ in Nigeria, a application with profound implications for overcoming one of public health’s most persistent challenges.

Even commonplace platforms like WhatsApp are being co-opted as AI-adjacent tools for improving coordination. The case study by Masresha et al. (2020) found the messaging platform to be an ‘effective tool for real-time coordination’ among health workers during immunization campaigns in Nigeria, demonstrating that low-tech, high-access solutions can yield significant operational benefits ( Gavi, 2024 ).

II. Critical Barriers and Implementation Gaps

Despite these promising applications, the literature uniformly identifies a suite of deep-rooted barriers that consistently impede the transition from successful pilot projects to integrated, scalable, and sustainable health solutions.

a) Foundational Infrastructural and Resource Deficits

The most fundamental barrier is the lack of robust technological and electrical infrastructure. As highlighted by Owoyemi et al. (2020) in their continental review, ‘infrastructure... gaps are major barriers’ to the adoption of AI for healthcare delivery. Unreliable internet connectivity, inadequate electricity supply, and low digital literacy effectively exclude large segments of the population, particularly in rural areas, from accessing AI-driven solutions. This directly creates ‘sampling bias’, as evidenced in Feng et al. (2018) ’s mobile phone survey in Sierra Leone, which explicitly ‘excludes those without phones’. An AI model trained on, or deployed for, a non-representative population risks being ineffective or, worse, exacerbating existing health inequities.

b) Data-Related Challenges: Quality, Availability, and Ethics

The lifeblood of AI is data, and here Africa faces a triple challenge. Li et al. (2024) emphasize the importance of operationalizing health data governance in low-resource settings, noting pilot initiatives in Zanzibar to establish AI-relevant data policies and frameworks. First, there is the issue of data quality and ‘noise in data’ ( Joshi et al., 2020 ). Social media scraping, while powerful, can be polluted by misinformation, making it difficult for algorithms to distinguish signal from noise ( Odlum & Yoon, 2015 ; Fung et al., 2016 ).

Second, there is a stark scarcity of large, curated, locally relevant datasets needed to train AI models effectively. Without these, models trained on data from other continents may perform poorly in the African context, a phenomenon known as algorithmic bias.

Third, and perhaps most critically, are the ethical questions surrounding data collection and ownership. The H3Africa Consortium (2015) directly addressed this by developing a policy framework for ‘ethical genetic data collection & sharing’, aiming to negotiate ‘fairness in genomics’. Infact, the H3Africa policy framework ( de Vries et al., 2015 ) provides a model for ethical genomic data sharing, underscoring that data derived from African populations should be governed by fair, locally-informed protocols. This work highlights the pervasive fear of exploitation and the urgent need for robust, locally-owned governance frameworks to ensure that data extracted from African populations benefits those same populations. The absence of such frameworks is a significant implementation gap.

c) Regulatory Fragmentation and Policy Vacuum

The rapid evolution of AI has far outpaced the development of corresponding regulatory structures. The Tech Governance Project ( TGov Team, 2024 ) describes Africa’s AI governance landscape as highly fragmented, highlighting inconsistent ethical standards and data privacy policies across countries. Townsend et al. (2023) ’s policy review meticulously mapped the ‘complex and varied regulatory environment across Africa’, identifying ‘regulatory gaps and fragmentation’ as key factors that ‘hinder implementation’. The absence of clear guidelines on data privacy, algorithmic accountability, and clinical validation creates an environment of uncertainty for developers and health authorities alike, stifling investment and deployment.

d) Financial Constraints and Sustainability Concerns

The development and maintenance of AI systems are capital-intensive. Major initiatives like the genomic surveillance systems require ‘major investment’ ( Makoni, 2020 ) and ‘continuous funding’ ( Botti-Lodovico et al., 2021 ) to remain operational. The heavy reliance on external donor funding raises serious questions about long-term ‘sustainability’ and ‘capacity building’ ( Makoni, 2020 ; Mboowa et al., 2024 ). Many projects risk becoming pilot studies that end when funding cycles conclude, failing to achieve the scale required for population-level impact.

e) The Scarcity of Local Capacity and Skills

The effective implementation of AI requires a skilled workforce of data scientists, software engineers, and bioinformaticians who understand both the technology and the public health context. The continental reviews by Owoyemi et al. (2020) and Mboowa et al. (2024) explicitly identify ‘skills’ gaps and the ‘ongoing need for capacity building’ as critical barriers. Without targeted investment in education and training, African institutions will remain dependent on foreign expertise, undermining local ownership and the development of context-specific solutions.

Windingly, the evidence is unequivocal: Artificial Intelligence holds formidable potential to revolutionise public health across Africa. From the retrospective detection of outbreak signals ( Joshi et al., 2020 ) to the proven efficacy of mental health chatbots in a randomised trial ( Kleinau et al., 2024 ), the successes documented are compelling and diverse. AI is no longer a futuristic concept but a present-day tool with demonstrated applications in surveillance, communication, and operational efficiency.

However, this discussion unequivocally argues that the primary impediment to realising AI’s full potential is not a lack of technical innovation but a constellation of structural and systemic barriers. The ‘evidence of success & impact’ is consistently tempered by ‘barriers & implementation gaps’ related to infrastructure, data governance, regulation, financing, and local capacity. The journey from a successful proof-of-concept to a scaled, sustainable public health utility is fraught with these non-technical challenges.

Therefore, the path forward requires a dual strategy. First, continued support for innovative research and piloting is essential to build the evidence base, as called for in reviews on chatbots ( Phiri et al., 2023 ; Mills et al., 2023 ). Second, and more critically, there must be a concerted, multi-stakeholder effort to address the foundational barriers. This entails investing in digital infrastructure, developing transparent and equitable data policies, harmonising regulatory frameworks ( Townsend et al., 2023 ), securing sustainable funding models, and most importantly, prioritising massive investment in local skills development and capacity building ( Owoyemi et al., 2020 ; Mboowa et al., 2024 ). Without this holistic approach, the risk is that AI will become another well-intentioned intervention that ultimately widens, rather than narrows, the health inequity gap. And on a final note, Fisher and Rosella (2022) has recommended that public health organizations need clear strategic priorities and governance frameworks to harness AI safely and effectively. The technology is ready; the task now is to build the ecosystems that allow it to thrive and serve all Africans.