AI’s Role in Preventing & Managing NCDs in Sub-Saharan Africa

The Silent Epidemic: Re-contextualizing the NCD Threat

The global health landscape is undergoing a profound shift, with non-communicable diseases (NCDs) now standing as the leading cause of death worldwide, responsible for over 70% of annual fatalities [User Query]. While historical public health efforts in Sub-Saharan Africa (SSA) have rightly focused on infectious diseases, a silent epidemic of NCDs is rapidly gaining momentum, presenting a new and complex challenge. The burden of these chronic illnesses—including cardiovascular diseases, cancers, diabetes, and chronic respiratory conditions—is disproportionately high in low- and middle-income countries, and SSA is no exception [User Query].

Demographic and lifestyle changes across the continent are accelerating this crisis. Rapid urbanization, evolving dietary patterns, and an aging population are contributing to a surge in NCD prevalence.¹ The data reveals the scale of this problem; between 1990 and 2017, the burden of NCDs in SSA grew by a staggering 67%, with their share of Disability Adjusted Life Years (DALYs) increasing from 18% to 30%. Projections are dire, indicating that by 2030, NCDs, injuries, and mental health conditions will cause more premature deaths than all other conditions combined, underscoring the urgent need for robust, data-informed interventions.²

Economic and Societal Impact: Why NCDs are a Development Issue

The rise of NCDs in SSA is not merely a health problem; it is a fundamental impediment to economic and social development. These chronic conditions pose a substantial economic threat due to lost productivity and soaring healthcare costs, placing immense strain on economies that can least afford it [User Query]. The region’s healthcare systems are already overburdened and largely ill-equipped to manage the long-term, chronic nature of these diseases.¹ A critical structural barrier is the severe shortage of qualified healthcare professionals. Africa has an average of only 1.55 physicians, nurses, and midwives for every 1,000 people, a figure far below the World Health Organization’s (WHO) recommended threshold of 4.45 healthcare professionals per 1,000 individuals necessary to effectively provide essential health services.³ This imbalance has profound consequences, as it leaves millions of people without timely or adequate care, particularly in rural regions where a single doctor may serve tens of thousands of patients.³

The escalating NCD burden is therefore caught in a powerful causal loop. The crisis is compounded by a severe shortage of healthcare workers and insufficient infrastructure, which in turn leads to inadequate management and treatment. This perpetuates a vicious cycle of poor health outcomes and economic strain. As the healthcare systems struggle, the economic burden of lost productivity and high costs grows, further draining resources that could be used to strengthen health infrastructure. This interwoven relationship means that addressing NCDs is central to achieving broader economic stability and progress. Artificial intelligence (AI) is uniquely positioned to break this cycle by enhancing the capacity of the existing workforce and extending the reach of care to underserved populations.³

The Potential of AI to Transform Patient-Centric NCD Care

AI as a Personal Health Partner: Empowering the Patient

In a context where physical access to healthcare is a luxury for many, AI, particularly through mobile health (mHealth) platforms, is emerging as a critical tool to empower patients and extend the reach of care.³ These solutions provide a lifeline for millions, delivering healthcare services remotely and bridging the gap between patients and professionals, especially in rural and geographically isolated areas.³ AI can be integrated into the healthcare ecosystem to improve diagnostics, streamline patient care, and personalize treatments, helping to transform healthcare from a privilege to a more accessible right.³

AI for Proactive Prevention

The traditional model of reactive care—where a patient only seeks help after symptoms become severe—is ill-suited for the chronic nature of NCDs. AI offers a paradigm shift toward proactive prevention and early intervention. AI models can analyze a patient’s medical history, lifestyle factors (such as obesity, tobacco use, and alcohol consumption), age, and sex to predict their risk of developing diseases like cancer, diabetes, and heart disease with a high degree of accuracy.⁸ A tool such as Delphi-2M, for example, can forecast future health outcomes and provide estimates of a person’s potential disease burden for up to 20 years.⁸ This provides individuals with an opportunity to make timely, informed lifestyle changes to mitigate their risk. At a population level, AI-powered predictive analytics can identify potential disease outbreaks or surges in NCDs, allowing governments and health organizations to allocate limited resources preemptively and improve emergency preparedness.³ A prime example of this is the Africa Centres for Disease Control and Prevention’s (Africa CDC) initiative to strengthen NCD surveillance across ten member states, which aims to equip health workers with the skills and systems needed to generate reliable data and inform evidence-based public health policies.²

AI for Enhanced Management

For those already living with NCDs, AI provides a new level of support for ongoing management. AI-powered telemedicine platforms and remote patient monitoring (RPM) systems leverage wearable devices and sensors to continuously track vital signs such as heart rate, blood pressure, and glucose levels.⁶ This enables early intervention and reduces the need for patients to travel long distances for appointments, making care more accessible and affordable.³ Furthermore, AI can function as a “co-pilot” for clinicians, providing real-time, evidence-based support aligned with national guidelines.¹⁰ This enhances the expertise of a limited workforce, automates repetitive tasks, and frees up healthcare providers to focus on the essential human interactions and emotional support that cannot be digitalized.¹¹ The Medbook AI system, integrated into the AphiaOne Hospital Management System in Kenya, exemplifies this approach, assisting with earlier diagnoses, smarter follow-ups, and reduced waiting times for patients living with diabetes.¹⁰ Patients gain a greater sense of control and confidence by having access to their own data, including lab results and prescriptions, which is crucial for improving adherence to treatment plans.³

AI for Behavioral Change

Lifestyle factors are the primary drivers of most NCDs. AI-driven applications and chatbots can serve as personalized guides, offering tailored advice on diet and exercise.¹² These tools can be programmed to use effective behavioral change techniques (BCTs) like goal setting, self-monitoring, biofeedback, and social support to promote healthier lifestyles and improve health outcomes.¹³ For example, the Stowelink Foundation’s NCDs 365 campaign uses multimedia and social media to provide daily health information, demonstrating how localized digital campaigns can be used to improve health literacy and drive behavior change across multiple countries.¹⁵ The proliferation of mobile technologies across the continent provides a ready-made foundation for these scalable, patient-facing solutions.³

Table 1: Key AI Applications in NCD Care for Patients in SSA

Application Area	Specific AI Technology	On-the-Ground Example	Patient/Provider Benefit
Predictive Risk	Machine Learning, Predictive Analytics	APHIAOne System, Medbook AI (Kenya) 10	Assists doctors with earlier, more accurate diagnosis; reduces complications for patients.
Remote Monitoring	Machine Learning, Wearable Sensors	Telemedicine/RPM platforms 6	Continuous monitoring of vital signs; enables early intervention without long-distance travel.
Clinical Decision Support	Natural Language Processing (NLP), Neural Networks	APHIAOne System, Medbook AI (Kenya) 10	Provides clinicians with an AI “co-pilot” for evidence-based support; improves workflow and reduces clinical errors.
Patient Empowerment & Education	Chatbots, mHealth Apps	Babyl (Rwanda), Afya Pap (Zimbabwe) 5	Offers remote consultations and personalized health coaching; gives patients control over their data; improves adherence to treatment plans.
Public Health Surveillance	Predictive Analytics, Data Modeling	Africa CDC Initiative 2	Identifies disease trends and potential outbreaks at a population level; enables proactive resource allocation.

A Critical Analysis of Barriers to AI Adoption in SSA

The Data Dilemma: From Scarcity to Bias

Despite the immense promise of AI, its widespread adoption in SSA is not without significant hurdles. The most central of these is the data dilemma, which is a complex problem of not just scarcity but also representation. AI algorithms rely on large volumes of high-quality data to train accurate and reliable models. However, most health data globally originates from developed countries, with only 1% coming from the African continent.¹⁸ Compounded by a low level of digitization and electronic medical record use, there is a profound paucity of locally generated, useful data for building AI systems tailored to African realities.¹⁹

This lack of data creates a critical and dangerous issue of algorithmic bias. Algorithms trained on data from predominantly white populations can fail when applied to African populations, leading to inaccurate diagnoses and potentially harmful or inadequate treatment recommendations.¹ This problem goes beyond simple inaccuracies; it is a matter of equity and patient safety. Without a concerted effort to build indigenous data ecosystems, AI risks exacerbating existing health inequalities rather than eliminating them. Therefore, the data challenge is not just about quantity but about quality, representation, and ethical risk, which requires a parallel effort to digitize health records and create representative data ecosystems.

Infrastructural and Financial Hurdles: Addressing the Digital and Energy Divides

The foundational requirements for AI adoption—digital infrastructure and reliable electricity—remain significant barriers. Low internet penetration, reported at 39% in 2020, and the fact that about half of all Africans lack access to electricity, make it difficult to execute and sustain digital health approaches, especially in rural areas.¹⁹ The financial costs associated with AI development are also prohibitive. Data acquisition, which involves cleaning and annotating data, can be costly and time-consuming, while the required hardware and computing resources, such as dedicated GPUs and cloud services, can incur significant running costs.¹⁹ With most African countries investing only a fraction of the global AI for health spending and often relying on external aid, scaling these solutions remains a persistent challenge.¹⁸

Navigating the Human Element: Bridging the Skills Gap and Combating Brain Drain

The human capital needed to develop and implement AI is also in short supply. Africa already suffers from a shortage of healthcare and technology professionals, a situation that is often worsened by brain drain as specialists migrate to developed countries in search of better opportunities.¹⁸ Even for those on the ground, a lack of confidence, knowledge, and skills in using new digital technologies can hinder adoption.²² Low digital literacy, particularly in rural communities, presents a major communication hurdle for the effective use of mHealth and AI-driven solutions.¹

The Ethical Imperative: Ensuring Fairness, Privacy, and Accountability

The absence of a robust regulatory framework in many African countries means that AI can be implemented without the necessary ethical and legal safeguards.¹ A major concern is the potential for AI to be used to spread dangerous misinformation, which erodes public trust in health information and technology. The deepfake scam in Nigeria, where AI-manipulated videos falsely promoted a nonexistent hypertension cure, serves as a potent example of a new, emerging threat vector.²⁴ The use of cloned voices and fabricated endorsements mimics trusted sources, potentially leading patients to abandon evidence-based care for serious chronic conditions. This is not just an isolated incident of fraud; it represents a systemic erosion of trust in the entire public health communication apparatus. Furthermore, there is a lack of clear legal guidance on who takes responsibility for adverse outcomes resulting from AI usage, creating a critical accountability gap.¹⁹

On-the-Ground Innovation: Case Studies and Emerging Ecosystems

Pioneering Local Solutions: Case Studies

Despite the significant challenges, a vibrant ecosystem of innovation is emerging across the continent, proving that AI is not a distant aspiration but a current reality. The key pattern revealed in successful projects is that they are context-specific and solve a clearly defined, local problem. In Kenya, the Medbook AI system, supported by the Bill & Melinda Gates Foundation, has been seamlessly integrated into the AphiaOne Hospital Management System. It provides doctors at Machakos Level 5 Hospital with a real-time “co-pilot,” assisting with earlier diagnoses, smarter follow-ups, and reduced waiting times for patients.¹⁰

Another success story is in Zambia, where a study demonstrated that an AI system for diagnosing diabetic retinopathy showed “clinically acceptable performance” when compared with human assessments.¹⁸ This illustrates AI’s potential to augment the capabilities of a limited specialist workforce, especially in a region with a dire shortage of healthcare professionals. AI-powered telemedicine is also filling critical gaps. Babyl in Rwanda has deployed an AI-enabled chatbot for personalized health coaching and non-clinical therapies for over two million Rwandans.⁵ Similarly, Zimbabwe’s Afya Pap offers a chatbot that dispenses medical advice and enables direct patient-to-doctor communication, helping to bridge the gap left by staff shortages.⁵ These examples suggest that the path to scaling AI in Africa is a “bottom-up” process of local innovation and targeted problem-solving, rather than a “top-down” approach of importing complex, foreign models.

The Rise of African Tech Hubs

The proliferation of these projects reflects a growing momentum. The past decade has seen a significant uptick in AI activity, with over 2,400 AI-focused companies operational across the continent by 2023.²⁶ Startups like Zencey, Eden Care, and mDoc are leveraging mobile technologies, machine learning, and big data to create innovative, locally tailored solutions.³ The SSA region is also seeing investments in foundational infrastructure, such as a plan to install 12,000 AI GPUs across five regional centers, which positions the continent not just as a consumer of AI but as a creator of the technology.²⁷ This is a critical step toward building a self-sustaining digital future.

Public-Private Partnerships and Donor-Funded Initiatives

The growth of AI in Africa is being catalyzed by strategic public-private partnerships and donor-funded initiatives. Philanthropic organizations and donors, such as the Gates Foundation and the Global Fund, are playing a catalytic role by funding AI projects for disease surveillance, diagnostics, and health system strengthening.⁴ Furthermore, institutional prioritization is evident. The Africa CDC, in partnership with the Novo Nordisk Foundation, has launched a two-year initiative to strengthen public health workforce capacity for NCD surveillance across ten member states, demonstrating a clear commitment to addressing the rising burden of NCDs.² These efforts are crucial for providing the financial and structural support needed to transition from small-scale pilots to scalable, impactful solutions.

A Strategic Roadmap for Responsible AI Integration

For AI to realize its full potential in strengthening African health systems and addressing the NCD burden, a clear, multi-pronged strategic roadmap is required. This blueprint must focus on building foundational pillars, fostering a conducive policy environment, and investing in human capital.

Building Foundational Pillars

The most critical first step is to address the underlying infrastructural and data deficiencies. Governments should prioritize investments in public digital infrastructure and access to electricity, as these are the fundamental prerequisites for AI adoption at scale.¹⁸ By focusing on public digital infrastructure, nations can ensure equitable access and avoid the market suffocation that can result from purely private investment.²¹ Alongside this, there must be a concerted effort to strengthen indigenous data ecosystems. This requires digitizing health records, establishing standardized data collection and storage systems, and creating data governance frameworks that ensure data is representative of local populations while protecting patient privacy.¹

Fostering a Conducive Policy Environment

Governance is paramount for ensuring that AI is deployed ethically and equitably. Governments must develop clear governance pathways and legal frameworks for AI in healthcare.¹ These frameworks must address key ethical concerns, including data privacy, algorithmic bias, and the complex question of accountability for adverse outcomes.⁴ A mandatory “human-in-the-loop” approach, where trained healthcare professionals always oversee AI systems, should be a key component of these policies.⁴ Additionally, co-designing solutions with local communities and ministries is essential to ensure that the technology reflects local realities rather than external assumptions.⁴

Investing in Human Capital

AI is a tool that requires a skilled workforce to be used effectively. Therefore, it is essential to invest in human capital by integrating AI training into medical curricula.⁹ This will build a specialized workforce capable of using AI tools skillfully and responsibly, preventing the erosion of clinical skills and fostering trust among healthcare professionals.³⁰ Furthermore, a parallel effort is needed to strengthen digital literacy among the general public through open education and civic engagement. This is crucial for building public trust and combating the spread of misinformation, which poses a significant and growing threat to public health.²⁸

Table 2: Critical Challenges and Mitigating Strategies

Challenge	Description	Strategic Pillar	Mitigating Strategy
Data Scarcity & Bias	Lack of large, locally-generated, and representative datasets for training models.	Building Foundational Pillars	Strengthen indigenous data ecosystems by digitizing health records and implementing standardized collection systems.
Infrastructural Deficits	Low internet penetration, limited electricity access, and high cost of hardware.	Building Foundational Pillars	Prioritize investment in public digital infrastructure and energy access to enable widespread adoption.
Ethical & Legal Gaps	Absence of robust regulatory frameworks for data privacy, bias, and accountability.	Fostering a Conducive Policy Environment	Develop and implement clear, ethical governance and legal frameworks for AI in healthcare.
Public Trust & Misinformation	The vulnerability of the public to AI-generated health scams and fake news.	Fostering a Conducive Policy Environment	Promote public digital literacy and co-design solutions with local communities to build trust and ensure relevance.
Skills & Workforce Gap	Shortage of technical and healthcare professionals with AI knowledge; risk of brain drain.	Investing in Human Capital	Integrate AI training into medical and public health curricula to build a specialized, local workforce.

Conclusion: Charting a Path Toward a Healthier, More Equitable Future

The rising burden of non-communicable diseases in Sub-Saharan Africa presents an existential threat to public health and socioeconomic development. The confluence of demographic shifts and a chronically under-resourced healthcare infrastructure creates a profound and urgent need for innovative solutions. AI is uniquely positioned to address this crisis by enhancing diagnostics, extending the reach of care, and empowering patients with tools for proactive prevention and management.

However, the promise of AI can only be realized if it is integrated thoughtfully and responsibly. The analysis indicates that the path forward is not a simple one of technological adoption, but rather a complex journey that must prioritize foundational infrastructure, ethical governance, and human capital development. The data shows that the most successful projects are those that are locally-led, context-specific, and seamlessly integrated into existing systems.

The future of health in Africa will be defined by the choices made today. By embracing a strategic and responsible approach, the continent can leverage AI to not just treat disease but to build more resilient, equitable, and patient-centered health systems. This is not just about technology; it is about charting a course toward a new public health order for Africa.²

Works cited

1. Artificial Intelligence in Early Detection and Risk Prediction of Non-Communicable Diseases in Sub- Saharan Africa – Tekigate Publisher, accessed September 25, 2025, https://journal.tekigate.com/jdai/archive/v1i2/a3/p3.pdf
2. Africa CDC Launches Initiative to Strengthen NCD Surveillance in 10 East and Southern African Countries – fundsforNGOs News, accessed September 25, 2025, https://news.fundsforngos.org/2025/09/24/africa-cdc-launches-initiative-to-strengthen-ncd-surveillance-in-10-east-and-southern-african-countries/
3. Transforming African Healthcare with AI: A Look at Emerging Startup, accessed September 25, 2025, https://aaeafrica.org/regional/transforming-african-healthcare-with-ai-a-look-at-emerging-startup/
4. The Ethics of AI-Driven Health Projects in Africa | Think Global Health, accessed September 25, 2025, https://www.thinkglobalhealth.org/article/the-ethics-of-ai-driven-health-projects-in-africa
5. Emerging Lessons on AI-Enabled Health Care – African Center for Economic Transformation, accessed September 25, 2025, https://acetforafrica.org/research-and-analysis/insights-ideas/commentary/emerging-lessons-on-ai-enabled-health-care/
6. Transforming African Healthcare with AI: Paving the Way for Improved Health Outcomes, accessed September 25, 2025, https://www.jscimedcentral.com/jounal-article-info/Journal-of-Collaborative-Healthcare-and-Translational-Medicine/Transforming-African-Healthcare-with-AI:-Paving-the-Way-for-Improved-Health-Outcomes-11760
7. Mobile technology and mHealth: The newest frontline in health care innovation in Africa, accessed September 25, 2025, https://www.brookings.edu/events/mobile-technology-and-mhealth-the-newest-frontline-in-health-care-innovation-in-africa/
8. New AI tool can predict a person’s risk of more than 1,000 diseases, say experts, accessed September 25, 2025, https://www.theguardian.com/science/2025/sep/17/new-ai-tool-can-predict-a-persons-risk-of-more-than-1000-diseases-say-experts
9. The Role of Artificial Intelligence in Strengthening Healthcare Delivery in Sub-Saharan Africa: Challenges and Opportunities – ResearchGate, accessed September 25, 2025, https://www.researchgate.net/publication/388224732_The_Role_of_Artificial_Intelligence_in_Strengthening_Healthcare_Delivery_in_Sub-Saharan_Africa_Challenges_and_Opportunities
10. AI in Healthcare: Transforming Diabetes Care in Kenya | Medbook, accessed September 25, 2025, https://www.medbookafrica.com/ai-in-healthcare-kenya-diabetes/
11. Digital Transformation of Public Health for Noncommunicable Diseases: Narrative Viewpoint of Challenges and Opportunities, accessed September 25, 2025, https://publichealth.jmir.org/2024/1/e49575/
12. Behavior Change App for Self-management of Gestational Diabetes: Design and Evaluation of Desirable Features – JMIR Human Factors, accessed September 25, 2025, https://humanfactors.jmir.org/2022/4/e36987
13. Effective behavioral change techniques in m-health app supported interventions for glycemic control among patients with type 2 diabetes, accessed September 25, 2025, https://pmc.ncbi.nlm.nih.gov/articles/PMC11907558/
14. An mHealth Intervention Promoting Physical Activity and Healthy Eating in a Family Setting (SMARTFAMILY): Randomized Controlled Trial, accessed September 25, 2025, https://mhealth.jmir.org/2024/1/e51201/
15. mHealth Interventions for Health Promotion in Sub-Saharan Africa: Assessing Impact on Health Literacy and Comparative Analysis o – Scholar Publishing, accessed September 25, 2025, https://journals.scholarpublishing.org/index.php/BJHR/article/download/18109/11024/26983
16. Non Communicable Diseases – Stowelink Foundation, accessed September 25, 2025, https://stowelink.com/non-communicable-diseases/
17. Improving Health in Africa through Mobile Technology | Wilson Center, accessed September 25, 2025, https://www.wilsoncenter.org/blog-post/improving-health-in-africa-through-mobile-technology
18. Artificial intelligence in global health: An unfair future for health in Sub-Saharan Africa? – PMC – PubMed Central, accessed September 25, 2025, https://pmc.ncbi.nlm.nih.gov/articles/PMC11823112/
19. Artificial Intelligence for Healthcare in Africa – Frontiers, accessed September 25, 2025, https://www.frontiersin.org/journals/digital-health/articles/10.3389/fdgth.2020.00006/full
20. AI in Healthcare in Africa – The Future Society, accessed September 25, 2025, https://thefuturesociety.org/wp-content/uploads/2022/02/AI-in-Healthcare-in-Africa-TFS-for-Mo-Ibrahim-Foundation-Forum-Report_vf.pdf
21. Transcript: Global AI Summit, accessed September 25, 2025, https://www.washingtonpost.com/washington-post-live/2025/09/24/transcript-global-ai-summit/
22. Impact and Challenges of Artificial Intelligence Integration in the African Health Sector: A Review – Content Management System, accessed September 25, 2025, https://tmr.scione.com/cms/fulltext.php?id=144
23. Will Africa Have a Seat at the Table of AI Regulation in Healthcare Development? – Medium, accessed September 25, 2025, https://medium.com/@atundearemo/will-africa-have-a-seat-at-the-table-of-ai-regulation-in-healthcare-development-80e85cb5a71f
24. Incident 1033: Video Allegedly Altered by AI Reportedly Spreads Claim of Nigerian Doctor’s Hypertension Cure, accessed September 25, 2025, https://incidentdatabase.ai/cite/1033/
25. Ignore AI-generated video of Nigerian celebrities promoting …, accessed September 25, 2025, https://africacheck.org/fact-checks/meta-programme-fact-checks/ignore-ai-generated-video-nigerian-celebrities-promoting
26. Artificial Intelligence in Sub-Saharan Africa, accessed September 25, 2025, https://aiinafricaresearch.alueducation.com/wp-content/uploads/2025/04/Overall_RGB.pdf
27. Africa’s AI Revolution Strive Masiyiwa & Cassava Technologies Build 12,000 GPU AI Factories – YouTube, accessed September 25, 2025, https://www.youtube.com/watch?v=rOhZJIwSEWc
28. Ethics of Artificial Intelligence | UNESCO, accessed September 25, 2025, https://www.unesco.org/en/artificial-intelligence/recommendation-ethics
29. Integrating artificial intelligence into African health systems and emergency response: Need for an ethical framework and guidelines – PMC, accessed September 25, 2025, https://pmc.ncbi.nlm.nih.gov/articles/PMC11966719/
30. Making AI usable, useful and safe – for clinicians and patients – Juta MedicalBrief, accessed September 25, 2025, https://www.medicalbrief.co.za/making-ai-usable-useful-and-safe-for-clinicians-and-patients/