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Impact and Challenges of Artificial Intelligence
Integration in the African Health Sector: A Review
1,2,3,10Elijah Kolawole Oladipo, 1Stephen Feranmi Adeyemo, 2Glory Jesudara Oluwasanya, 1Omotayo Rachael Oyinloye,
1,4Olawumi Hezekiah Oyeyiola, 1,4Ifeoluwa David Akinrinmade, 1,4Olubunmi Ayobami Elutade, 1,5Dorcas Olayemi Areo,
1,5Islamiyyah Olamide Hamzat, 1,6Oluwakemi Deborah Olakanmi, 1,4Israel Ifeoluwa Ayanronbi, 1,6Akinwumi John
Akanmu, 1,5Faith Opeoluwa Ajekiigbe, 1,5Mary Olawumi Taiwo, 1,5Victor Michael Ogunfidodo, 1,7Christiana Adewumi
Adekunle, 1,4Precious Oluwadamilola Adeleke, 1,8David Ayo Olubunmi, 1,5Precious Ayomide Adeogun, 1,9Emmanuel
Oluwagbenga Adejobi, 1,5Samiat Arike Sanni, 3Akinola Oluwatosin Ajibade, 10Helen Onyeaka and 10,11Nnabueze
Darlington Nnaji
1Division of Medical Artificial Intelligence, Helix Biogen Institute, Ogbomoso, Oyo, Nigeria
2Division of Genome Sciences, Helix Biogen Institute, Ogbomoso, Oyo, Nigeria
3Department of Molecular Biology, Immunology and Bioinformatics, Adeleke University, Ede, Osun, Nigeria
4Department of Biochemistry, Ladoke Akintola University of Technology, Ogbomoso, Oyo, Nigeria
5Department of Pure and Applied Biology, Ladoke Akintola University of Technology, Ogbomoso, Oyo, Nigeria
6Department of Physiology, Ladoke Akintola University of Technology, Ogbomoso, Oyo, Nigeria
7Department of Science Laboratory Technology, Ladoke Akintola University of Technology, Ogbomoso, Oyo, Nigeria
8Department of Biology, Federal University of Technology, Akure, Ondo, Nigeria
9Department of Human Anatomy, Ladoke Akintola University of Technology, Ogbomoso, Oyo, Nigeria
10School of Chemical Engineering, University of Birmingham, Edgbaston, Birmingham B12 2TT, United Kingdom
11Department of Microbiology, University of Nigeria, Nsukka, Nigeria
ABSTRACT
Artificial intelligence has proven to be a game-changing force in health sectors throughout Africa offering prospects
for significant development. In sub-Saharan Africa, using AI in healthcare, especially in areas with limited resources,
holds valuable promise in transforming and improving healthcare. This article takes an excellent look at how AI is
being integrated into the African health sector, as well as examining policy frameworks, challenges and future
possibilities. This article begins by giving an overview of AI and highlighting the groundbreaking impact of AI
technologies in combating and addressing healthcare challenges that occur within African countries. Ranges from
mobile-based diagnostics to precision medicine, artificial intelligence has proven its potential and capabilities in
diagnosing, treating and improving healthcare operations by providing solutions to resource constraints and
accessibility challenges. However, despite these advancements, there are still obstacles such as infrastructure
limitations, concerns about data privacy and gaps in healthcare professionals’ training that hinder the realization of
AI’s potential in African healthcare. This article envisions a future where the adoption of artificial intelligence is fully
incorporated with community health initiatives and enhanced access to healthcare services for the betterment of
healthcare across sub-Saharan African countries. While challenges an d b ar ri ers l ik e i nf rast ru ct ure a nd un equa l a cc ess
to healthcare persist, there is a need for governments and stakeholders to prioritize intelligence and digital health
as catalysts for improving the healthcare sector in sub-Saharan Africa.
KEYWORDS
Artificial intelligence, sub-Saharan Africa, healthcare transformation, public health surveillance, disease
detection, telemedicine
Copyright © 2024 Oladipo et al. This is an open-access article distributed under the Creative Commons
Attribution License, which permits unrestricted use, distribution and reproduction in any medium, provided
the original work is properly cited.
ISSN: 2151-6065 (Online) Received: 07 Mar. 2024
ISSN: 1819-3587 (Print) Accepted: 10 Jun. 2024
https://doi.org/10.3923/tmr.2024.220.235 Published: 11 Jun. 2024
Page 220
Trends Med. Res., 19 (1): 220-235, 2024
INTRODUCTION
The healthcare sector in Africa stands as one of the most dynamic industries globally. Recent years have
witnessed a renewed global health community focus on strengthening health systems, recognizing that
systemic weaknesses significantly hinder the accomplishment of Millennium Development Goals (MDGs).
Many nations especially those in sub-Saharan Africa still require substantial development in their
healthcare industry, relying heavily on imports and seeking private investment support1. Enhancing the
health workforce, securing funding for robust health system improvements and expanding health system
knowledge would profoundly benefit the approximately 800 million people residing in the World Health
Organization (WHO) Africa region. National governments, along with civil society organizations (CSOs),
development partners and communities grap ple with th e cha llenge of providing healthcare across Africa.
Intensified efforts in preventive and curative services, along with health promotion, have been evident
since the inception of the MDGs2. According to Stothard et al.3, malaria emerges as a prevalent health
issue in sub-Saharan Africa, witnessing 225 million cases annually and resulting in approximately 781,000
deaths. To improve health service delivery in Africa, countries must increase access for the poor in peri-
urban and urban areas.
The AI in healthcare encompasses the use of machine learning algo r it hms a nd cogn i ti ve te c hn olog y wi thi n
medical settings, representing the convergence of human and machine learning4. The use of AI in
medicine dates back to the 1970s, with the development of medical expert systems that use Bayesian
statistics and decision theory to diagnose and recommend treatments for conditions like glaucoma and
infectious diseases5. As AI gains traction in various industries, including healthcare, it holds the potential
to revolutionize patient care. Employing computer technology to simulate intelligent behaviour akin to
human critical thinking, AI in healthcare is primarily utilized for diagnosing, prognosis and treating
diseases1.
This transformative technology analyzes extensive clinical documentation rapidly, aiding medical
professionals in identifying disease markers and trends that might be overlooked through traditional
methods. The groundbreaking emergence of AI in healthcare enhances the efficiency of healthcare
systems, making them smarter and faster in providing care to millions of outcomes AI’s use in hospitals
and clinics is shaping the future, reducing costs for providers and improving health outcomes6.
The integration of AI in healthcare significantly aids medical practitioners in various aspects of patient care,
extending to administrative procedures. As of 2020, the adoption of AI in the USA and Canada has
resulted in a 25% reduction in healthcare expenses in the former and a 12% decrease in the latter7.
The potential of AI to revolutionize healthcare in sub-Saharan Africa is substantial, particularly in
automating medical procedures and enabling health professionals to achieve more with limited resources.
The AI’s application extends to the evaluation of vast healthcare data, as demonstrated by a logistic
regression-based prediction model automating early diagnoses of c ar d ia c di s eas e s w i th pr o misi n g re s ul t s6.
Furthermore, AI technology has the potential to enhance patient care, le adin g to mor e pre cise diagnoses ,
including in surgical procedures. Noteworthy initiatives in Nigeria, such as the startup Ubenwa, utilize
advanced technologies such as machine learning to improve the diagnosis of birth asphyxia in
low-resource settings7. In Zambia, AI is employed for diagnosing diabetic retinopathy, the results obtained
are significant and promising when compared to human assessment8.
The AI is becoming increasingly prevalent in the healthcare industry, with applications ranging from
detecting pulse rates to diagnosing cancer and providing therapy consultations. Today, there are
continued developments, research and inventions in the application of AI across different branches of
healthcare. Therefore, this study aims to investigate the impact and challenges of integrating artificial
intelligence in the healthcare sector in sub-Saharan Africa.
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OVERVIEW OF AI IN HEALTHCARE
Artificial Intelligence (AI) is a system that can precisely understand external data, assimilate knowledge
from the data and employ the acquired learnings to achieve goals and tasks through adaptation9.
The AI comes in different forms and one way to categorize it is by considering its level of cognitive
capability. The three main types of AI classification include weak or limited artificial intelligence, often
referred to as "functional" AI, which is engineered to efficiently execute specific tasks without the capability
to reason generically or learn from novel situations10. This type of AI is tailored to solving particular
problems and lacks the adaptability to generalize its behaviour across various contexts. It finds application
in systems requiring repetitive tasks, such as filtering email spam, deriving generalizations from vast
datasets, offering recommendations on streaming platforms and making decisions in e-commerce
settings. A great example is Alexa, Amazon’s AI-powered voice assistant, which is considered an instance
of weak AI. Although, it exhibits apparent intelligence and responsiveness, its capabilities are limited to
sp eci fic tas ks l ike manag ing smart hom e devic es, re sponding to voice commands, playing music, offering
weather updates and setting reminders. However, Alexa is confined to a predetermined set of functions
and cannot adapt to novel or unfamiliar situations, setting it apart from more advanced forms of AI9.
General AI stands apart from weak AI by demonstrating a broad spectrum of cognitive abilities, including
reasoning, learning and problem-solving, with the capability to adapt to new situations. It focuses on
c re at in g com put ing s yst ems tha t ca n per for m a wi de r ange of t asks, closely emulating human intelligence.
Examples of general AI include autonomous weapons capable of autonomous learning and adaptation,
as well as advanced personal assistance systems like the GPT-3 chatbot, which offers highly rational and
adaptively intelligent responses. Strong AI, synonymous with general AI, envisions intellect comparable
to human capabilities, encompassing understanding, reasoning and adaptive actions under any
circumstances. While some consider strong AI to be theoretical, debates arise on its existence, especially
when self-awareness is considered a facet of human intelligence9,11.
The last type of AI under this class is ASI (artificial super intelligence) also known as high-performance or
strong AI, which possesses the capability to excel in virtually any task requiring human intelligence,
surpassing humans in cognitive and learning abilities11. In medical research, high-performance AI plays
a pivotal role in analyzing extensive medical datasets. An illustrative example is DeepMind, a company
owned by Google, which developed AlphaFold. The AlphaFold aids in predicting protein structure and
diagnosing and treating genetic diseases, such as Alzheimer’s, P ar k in s on’ s , Hu n tin g ton’ s and c yst i c fi b ro s is ,
that result from proteins folding incorrectly9. Incorrect protein folding can lead to various health issues.
AlphaFold’s detailed scrutiny of protein folding not only aids in precise disease diagnosis but also plays
a crucial role in developing targeted treatments. Essentially, artificial super intelligence (ASI) is reshaping
the field of medical research and healthcare by providing unprecedented insights and cutting-edge
solutions.
The healthcare sector is experiencing a revolutionary change with the rapid advancements in analytics
techniques and the abundant availability of healthcare data, there has been a surge in the healthcare
industry, all fueled by the progress of artificial intelligence12. The AI has been engaged in the field of
medicine since the 1950s when physicians initiated the earliest endeavours to enhance their diagnostic
capabilities through computer-aided programs. In recent times, AI’s impact on healthcare has sparked
debates about replacing human doctors10. The AI also plays an important role in aiding physicians to
enhance clinical decision-making. There is potential for AI even to take over certain specific functions in
healthcare, such as radiology, dermatology and pathology, where it can contribute to improved accuracy
and efficiency13. Other applications include the monitoring, evaluation and analysis of health status, health
promotion and research in public health.
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Secinaro et al.14 discussed the application of virtual reality technology in rehabilitative medicine.
The authors noted the main objective of rehabilitation is to improve and reinstate functional ability and
enhance the quality of life for individuals dealing with physical impairments or disabilities. The AI and deep
learning have the potential to assist medical and administrative personnel in extracting data, predicting
outcomes and acquiring knowledge of medical representations. The AI techniques can reveal clinically
pertinent information within extensive datasets, thereby aiding in clinical decision-making11. The AI has
the potential to revolutionize patient monitoring and healthcare delivery, particularly in the field of
wearable patient monitoring systems (WPMs). The AI platforms, now integral in public health initiatives,
rely heavily on robust data systems for effective health emergency preparedness12.
Despite this, Africa encounters challenges in accessing, analyzing and utilizing data for informed health
determination. Access to population data is restricted in many countries and even those with access find
it challenging to use it for program enhancements. With the rise in mobile phone ownership and usage
in Africa, the region is now in a strong position to leverage AI technologies to enhance the adoption,
access and utilization of health data13-15.
Globally, there has been a consistent demand for technology solutions due to the COVID-19 pandemic.
These solutions are used for tracking infections, minimizing direct human contact and screening
populations. Notably, technology has played a critical role in safeguarding medical personnel by
disinfecting controlled environments, reducing direct contact with patients and easy passage of public
health and emergency messages. In Africa, digital technology has the potential to improve crisis
management in the health sector, strengthen healthcare systems and enhance overall efficiency through
effective digital mechanisms14.
IMPACT OF AI INTEGRATION IN THE SUB-SAHARAN AFRICAN HEALTH SECTOR
Transformative power of AI in diagnostics and disease prediction: Introducing artificial intelligence
in the African health sector portrays a major and important development, particularly in the aspect of
diagnostics and disease prediction. This development is enhanced by an excellent understanding of
machine learning algorithms and sophisticated AI models, that propel the best prospects for combating
the complex issues of healthcare that are disturbing several continents16. In sub-Saharan Africa, where
specialized healthcare resources are often hard to find and access, AI is a perfect solution for enhancing
diagnostics. Because of its ability to process vast datasets, including medical structures and patient
records, it optimally enhances the accuracy and efficacy of the detection of disease. This is particularly
necessary in regions where early detection can be very crucial for successful treatment outcomes4,5.
The adaptive nature of AI is advantageous for curbing the variations and unique disease patterns
prevalent in several African regions. Diseases often manifest differently across continents and AI’s capacity
for progressive learning ensures that diagnostic abilities evolve, maintaining pace with the developing
nature of diseases16,17.
Ada Health’s mobile application, a one-symptom checker for all medical problems powered by AI, is
making substantial strides in diagnostics improvement in settings with limited resources. In areas with
limited access to specialized medical experts, Ada Health utilizes machine-learning algorithms for
symptom analyses and the provision of preliminary diagnoses. This helps individuals find timely medical
care and attention and aids healthcare workers in making relevant informed decisions, thereby enhancing
their overall diagnostic abilities18.
Also, the Malaria Scope project, specifically for malaria prediction and mapping is a very good example
of how AI can contribute to the prediction of disease, especially in the context of malaria outbreaks in
Africa. The project revolves around making use of AI for the analysis of various datasets, ranging from
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climate data and travel patterns, to historical malaria cases, to predict and map potential malaria
outbreaks. This enables proactive measures and optimizes the allocation of resources for prevention and
treatment strategies, solving the diverse challenges posed by malaria in different sub-Saharan African
countries19. A published study in the Malaria Journal in 2010 also used the “Genetic Algorithm for Rule-set
Prediction” (GARP) model to map malaria vector species in Africa based on actual findings20.
These examples portray how AI and data analysis are being carried out to predict and map malaria
outbreaks, optimally contributing to more effective prevention and treatment strategies in attacking
malaria in Africa.
In South Africa, AI algorithms are being employed to analyze chest X-rays for early detection of
tuberculosis. These algorithms, using a deep learning (DL) approach and already trained on various
datasets, can recognize subtle patterns indicative of tuberculosis and aid healthcare workers in initiating
treatment and early or timely diagnosis21. The use of AI for detecting tuberculosis in chest X-rays is a great
development, especially in countries where tuberculosis remains a major health issue. The AI approaches
in healthcare diagnostics are transforming the field through the provision of faster and more accurate
analysis of medical data. Its ability to differentiate between cunning variations in medical data, especially
in exploring intricate medical images and recognizing patterns that might go unchecked in traditional
diagnostic approaches, enhances diagnostic accuracy and hastens the identification of potential health
risks, aiding more timely and effective healthcare interventions22.
T ra ns f or ma tive i nf lu enc e of AI in t eg r at ion o n t r ea tme n t a n d p ersonalized medicine: T he i nt e rs e ct i on
of AI and healthcare establishes a revolutionary era, redefining treatment modalities and ushering in a new
age of personalized medicine. In the diverse landscape of the sub-Saharan African health sector, the
evolution of AI promises profound impacts on the diagnosis and treatment of several diseases, with a
major emphasis on infectious diseases4. The AI plays a major part in progressing precision medicine,
offering a radical change in the understanding and curing of infectious diseases23. One of the potent tools
empowered by AI is Nuclear Magnetic Resonance (NMR), which provides early detection capabilities that
transcend conventional diagnostic timelines. The amalgamation of AI and radiological diagnosis,
particularly in the context of pulmonary tuberculosis, amplifies diagnostic accuracy and expedites the
identification of affected individuals. This precision-driven approach not only facilitates more nuanced and
ta rge ted treatmen t st rat egi es b ut also ho lds the promise o f curbing the spread of infectious diseases4.
As 54 genes, a pharmaceutical company in Nigeria and LifeQ in South Africa use artificial intelligence to
analyze genomic and biometric data to deliver personalized insights and recommendations for health and
wellness23,24. The intersection of AI and precision medicine can transform healthcare by enhancing the
personalization of treatment for each person. This requires access to massive amounts of data, such as
data collected through projects like the UK Biobank and the “All of Us” project, to create personalized
treatment plans focusing on individual exceptional characteristics25.
The utilization of AI in early diagnosis and detection is very beneficial in the African health sector.
By connecting the analytical prowess of AI algorithms, healthcare systems can scrutinize massive datasets
to discern subtle patterns indicative of various infections. This proactive approach empowers healthcare
providers to intervene swiftly, mitigating the contagious spread of diseases and significantly improving
the prognosis for affected individuals. In the realm of viral upper respiratory infections, where rapid
identification is pivotal, AI emerges as an indispensable ally in bolstering early diagnosis efforts4,26.
The AI algorithms can accurately analyze medical images, such as MRIs, CT scans and X-rays, detecting
subtle anomalies that may be missed by human observers. This capability has been instrumental in the
early detection of conditions like cancer26. Additionally, AI can be used to monitor the vital signs of
patients in real-time, detecting high-risk cases and allowing for early intervention, leading to improved
outcomes, decreased healthcare costs and saved lives.
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The AI’s ability to analyze and interpret complex data sets, including patient health records, genetic
information and treatment outcomes, positions it as a crucial factor in optimizing drug choices and
treatment regimens. By analyzing diverse patient data, AI can discern optimal treatment options based
on individual characteristics, thereby minimizing adverse drug effects while maximizing therapeutic
efficacy27. This tailoring of interventions raises a more patient-centric and efficient healthcare paradigm25.
The AI’s integration into healthcare can lead to more effective treatment of common conditions or rare
diseases and allow for optimiza tion of the timing and dosage of me dication for individual patients.
This personalized approach could lead to earlier diagnosis, prevention and better treatment, saving lives
and making better use of resources27. As these advancements gain momentum, the narrative of healthcare
in Africa is poised to undergo a paradigm shift, marked by enhanced diagnostic precision, personalized
treatment strategies and ultimately, improved health outcomes for diverse populations.
Streamlining healthcare operations in the sub-Saharan African health sector: Artificial intelligence
(AI) has a high probability of improving the way healthcare works in sub-Saharan Africa, curing
health-related problems and enhancing the overall effectiveness and efficacy of healthcare delivery.
In Rwanda, Zipline, which is a US-based health logistics company, utilizes drones to overcome the
hindrance of mountainous terrains, ensuring efficient delivery of medical supplies to rural health clinics28.
This approach not only tackles accessibility issues but also establishes a precedent for using technology
to surmount logistical obstacles in healthcare delivery. The AI technologies have also shown promise in
various aspects of healthcare, presenting innovative solutions that can be particularly beneficial for
resource-constrained settings such as those found in many parts of Africa. Across Africa, AI is aiding
healthcare in important aspects such as overseeing medical data files in Morocco, the study of genomes
in South Africa, COVID-19 tracking in Ethiopia and analyzing medical images in Ghana29.
Because of the decrease in the availability of medical professionals across much of Africa, AI can probably
fill voids left by doctors and other highly skilled health professionals. By 2030, it is estimated that AI will
bring economic growth worth $1.2 trillion (about $3,700 per perso n in th e U S) to Af ri ca . H oweve r, ke eping
AI projects within Africa is difficult as many doctors move to developed countries. Locally driven and
owned AI solutions that prioritize safety, equi ty, transp arency, reliability and societal benefit are essential.
Governments in sub-Saharan African nations must develop legislation and policies that will govern AI’s
adoption in the healthcare sector30. Th e A I c an sign if ic an tly ai d i n opti mi zing t he alloc at ion of hard-to-find
healthcare resources. In South Africa, AI-driven predictive analytics has been found as a way of
determining pandemic symptoms and excellent distribution of medical resources in cases of public health
emergencies. This technology, therefore, enables the analysis of data for the prediction of disease
outbreaks, portraying high-risk areas and streamlining medical supplies, likewise, enhances resource
management and sustainable farming practices such as monitoring crops, analyzing soil and optimizing
supply chains31.
These artificial intelligence tools can probably have considerable effects on public health surveillance and
monitoring by providing real-time population health monitoring, thus facilitating rapid responses to
epidemics as well as proactive public health measures. For example, the Kenyan government examined
the use of AI for live disease surveillance that allows for timely interventio n and containme nt efforts32.
The AI could be useful in analyzing epidemiological data to identify patterns and trends hence providing
a more precise and faster detection of disease outbreaks and informing targeted therapies. Additionally,
AI may be applied in public health surveillance not just in specific regions but also elsewhere to enhance
surveillance and response capacity32. The use of AI for public health surveillance can potentially improve
disease detection, responses and general healthcare management. Therefore, using AI in public health
surveillance holds promise for enhancing disease detection, response and overall public health
management.
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Table 1: Impacts of AI integration in the sub-Saharan African health sector
AI integration Specific countries References
Diagnosis and disease prediction
Mobile-based diagnostics (ADA mobile Swahili-speaking countries (Kenya, Tanzania Ellahham18
health application) and Uganda)
Malaria prediction and mapping sub-Saharan African countries (South Africa, Santosh and Gaur29
Gambia and Kenya)
Tuberculosis detection in chest X-rays South Africa Rajakumar et al.21
Treatment and personalized medicine
Precision medicine (NMR, 54 gene, LifeQ) Nigeria and South AfricaOtaigbe
4, Schork23
and Fatumo et al.24
Early detection and diagnosis (upper Tanzania Otaigbe4
respiratory tract infections)
Optimizing drug choice and treatment regimens South Africa Johnson et al.25
Streamlining healthcare operations
Zipline logistics company Rwanda Amukele28
Resource allocation optimization South Africa Chilunjika et al.31
Public health surveillance and monitoring Kenya Taylor-Robinson32
Strengthening public health systems
Telemedicine and remote patient monitoring Kenya Betjeman et al.35
(M-Tiba mobile health platform)
Healthcare workforce support (babylon Rwanda Santosh and Gaur29
chat-bot)
Drug discovery and personalized medicine South Africa and NigeriaMak et al.38
AI and infectious disease surveillance Nigeria Otaigbe4
(EpiAFRIC)
Barriers to AI integration in the sub-Saharan African health sector
Strengthening public health systems in the sub-Saharan African health sector: The integration of AI
in the sub-Saharan African healthcare sector has the potential to revolutionize healthcare delivery,
enhance efficiency and improve patient outcomes. Table 1, details the impacts of AI integration in this
region. The African healthcare landscape is faced with numerous challenges, including limited resources,
insufficient infrastructure and a high burden of infectious and non-communicable diseases. The AI
presents an opportunity to curb these issues7,33. The AI plays a significant role in expanding access to
healthcare through Remote Patient Monitoring (RPM) and Telemedicine. The RPM contributes to the
improvement of health outcomes, especially for patients with neurological and cardiovascular diseases34.
Also, healthcare professionals can organize virtual consultations and remotely monitor patients’ vital signs
by utilizing AI-driven telehealth platforms, leading to improved access to healthcare services and more
effective management of chronic conditions. The M-Tiba mobile health platform in Kenya uses AI to
facilitate telemedicine consultations and remote monitoring, enabling patients to consult with healthcare
professionals through their mobile phones35. The AI algorithms help in tracking and managing chronic
conditions, ensuring timely intervention and decreasing the need for consistent physical hospital visits,
majorly in remote and underdeveloped areas where there is limited access to medical facilities36.
The shortage of healthcare professionals in many African countries is a significant challenge. The AI can
act as a force multiplier by supporting the existing healthcare workforce. The AI applications, for example,
chatbots and virtual health assistants, can provide information and offer basic medical advice, thereby
easing the burden and improving the efficiency of healthcare delivery37. Babylon Health’s AI-driven
chatbot has been integrated into the healthcare system in Rwanda, providing instant medical information,
advice and assistance to patients. The chatbot helps to relieve some of the pressure on healthcare
professionals and improve access to basic healthcare information for the population. The adoption and
development of AI chatbots in Africa have been influenced by several significant global health trends and
AI chatbots have been used in some parts of Africa to effectively fight the Ebola virus29.
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Also, the African Drug Discovery Foundation (ADDF) uses AI algorithms to accelerate drug discovery for
diseases prevalent in sub-Saharan Africa, such as malaria and tuberculosis. By analyzing genetic data and
simulating molecular interactions, AI expedites the identification of potential drug candidates, leading to
more efficient drug development processes. The use of AI in drug discovery processes is transforming the
traditional resource-intensive and time-cost methods38. The AI’s integration in drug discovery is projected
to enter a new age, with the global market estimated to be worth $4.9 billion (about $15 per person in
the US) by Paul et al.39. Several significant global health trends have influenced the adoption and
development of personalized medicine in Africa and it has been used in some parts of Africa to optimize
the choice of drug and dosage while avoiding adverse effects for the specific patient. The concept of
personalized medicine in sub-Saharan Africa is still developing and it is vital to apply it to overcome
various challenges40. The use of AI in personalized medicine can help in the analysis of patient data to
develop personalized care plans, leading to more effective treatment outcomes38,40.
Recently, in Nigeria, the EpiAFRIC organization has utilized AI in disease surveillance by analyzing diverse
datasets, including social media, climate information and travel patterns. The EpiAFRIC’s AI algorithms
contribute to the early detection of infectious disease outbreaks, allowing for rapid response and
containment strategies4,41. The employment of AI in disease surveillance has proven valuable in various
infectious diseases surveillance and control, such as tuberculosis, malaria, Ebola viral hemorrhagic fever
and HIV/AIDS. The integration of AI in disease surveillance promises to revolutionize healthcare delivery,
enhance efficiency and improve patient outcomes strategies
41. These examples demonstrate how AI
technologies are actively aiding the improvement of healthcare in Africa, curbing specific challenges and
providing innovative solutions that bring progress to the overall effectiveness and efficiency of public
health systems on the continent. The employment of AI holds immense ability to transform healthcare
delivery, optimally contributing to better health outcomes and the well-being of African populations.
Infrastructure and technology challenges: Infrastructure and technology challenges pose significant
barriers to the implementation of AI in sub-Saharan Africa. The challenges in assessing bias in AI
algo rithms are made more d if ficult because many of th ese algorit hm s are considered ‘black boxes’.
This means that it’s harder to determine if they are biased or not. However, some researchers are trying
to assess biases by testing how well the algorithms predict outcomes when they randomly change key
variables for individuals42. The lack of adequate ICT infrastructure, including low internet penetration rates
and limited access to high-performance computers, makes it difficult for healthcare systems to adopt and
implement AI systems43.
The cost of purchasing, maintaining and upgrading high-performance computers is often prohibitively
expensive for healthcare systems that rely on funding42. Many sub-Saharan African countries lack policies
to direct e-health development and dissemination in public hospitals, affecting digital health adoption42.
The progression of technology frequently outpaces regulatory frameworks, which can hinder the adoption
of digital health initiatives. A recent investigation into e-health policies across four African nations revealed
that the strategic objectives were ambiguous an d lacked cohesive strategies. The lack of clear policies for
e-health development in African countries has been identified as a significant barrier to the adoption of
digital health solutions5.
In sub-Saharan Africa, particularly in rural areas, low internet penetration rates and limited access
to high-performance computers pose significant challenges to the deployment of AI applications in
healthcare settings5. F or i ns t an c e, i n r ura l Ug a nd a , s par s e i n te r net c o nnec t ivit y hinders the implementation
of AI-powered telemedicine platforms, limiting the reach of remote healthcare services to underserved
communities. According to Statista, the number of internet users in Uganda is forecast to amount to
7.48 million in 2024, with an estimated internet penetration of 14.99%44-46. The data underscores the
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notable gap in digital access and the hurdles linked to internet availability in the area. These obstacles
profoundly affect the capacity to utilize AI innovations in healthcare, given the dependence on strong
internet connections and advanced computing capabilities44,45.
The financial constraints associated with the acquisition, insta ll a ti on a nd m ai n te n an ce o f hi g h- p er f or m an c e
computers present formidable challenges for healthcare systems across sub-Sahara Africa, particularly
those heavily reliant on donor funding37. A poignant example of these financial barriers is evident in
Nigeria, where the scarcity of financial resources has significantly impeded the integration of AI-driven
diagnostic tools within public health facilities16. Addressing infrastructure and technology challenges in
Africa will require significant investment in ICT infrastructure, digital skills training for healthcare
professionals and the development of policies and frameworks to support the integration of AI-based
solutions into health systems43.
Data privacy and security concerns: An individual’s personal identity number, mobile number, voice,
image and other forms of identity are frequently included in the healthcare database. Many data points
including sensitive personal information are needed to develop an AI-powered medical gadget, however,
getting hold of such sensitive data could result in privacy-related legal problems47. Additionally, the lack
of transparency regarding how companies handle patient data raises co nc er ns ab ou t t he po tent ia l m is use
of patient information. These security concerns highlight the need for proactive measures to safeguard
patient data and ensure transparency in data handling practices within the healthcare industry48. The
absence of clear policies and legislation to safeguard privacy while enabling critical analysis of health data
is identified as a constraint in the Kenyan health sector49. However, Kenya has taken measures to address
data privacy issues by issuing new guidelines and establishing an Office of the Data Protection
Commissioner to enforce compliance with data privacy laws. Challenges related to the handling of patient
information and the need for robust data protection measures are part of a broader context of utilizing
digital health to overcome Africa’s health issues49.
Also, the 2019 data breach in South Africa’s health database exposed millions of sensitive information,
including personal identity numbers and medical records50. The incident portrayed immediate threats to
data privacy and underrated the benefits and importance of the need for strict security measures in
handling healthcare data. The breach occurred on Jigsaw Holdings, the server of a property company,
which was traced through an IP address. The incident is a reminder that strong security measures are
crucial for protecting sensitive healthcare data and maintaining individual privacy. The study emphasizes
the need to address ethical considerations, particularly privacy when using and deploying AI applications
in healthcare. This can help healthcare companies understand the AI market and obtain different
indications of how stable, profitable and valuable it is to provide better services to their customers51.
Healthcare workforce and training acceptance: The integration of AI in the African health sector
encounters significant barriers as outlined in Table 2. One of the foremost challenges is the lack of
specialized expertise and digital skills among healthcare workers. It is challenging to implement mobile
health smart applications in developing nations with limited resources since many healthcare practitioners
lack knowledge and skills related to digital health. The integration of AI-based models into health systems
is similarly hampered by inadequate frameworks and policies that facilitate the integration of data-driven
AI-based solutions38. A study conducted in rural Uganda revealed that low levels of computer knowledge
among health workers hinder the successful adoption of electronic clinical decision support systems
(CDSSs)52. The findings in this study stated that health workers have limited computer knowledge, with
proficiency only in Microsoft Word. The study also revealed that health workers faced challenges in using
ICT when they were assigned the task of obtaining Tax Identification Numbers alongside other staff.
The lack of computer knowledge among health workers in rural areas has proven to be a significant barrier
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Table 2: Barriers to AI integration in the African health sector
Barriers Specific countries References
Infrastructure and technology challenges
Low internet penetration and limited access to Uganda and Kenya Owoyemi et al.5
High-performance computers
Prohibitive cost of high-performance computers Nigeria Takuwa et al.46
Lack of clear policies for e-health development Kenya Owoyemi et al.5
Data privacy and security concerns
Concerns about patient information handling Kenya Mbuthia et al.49
Health database reach South Africa Neto et al.50
Healthcare workforce and training acceptance
Lack of confidence, knowledge and skills in using ICTs Ghana and Uganda Yagos et al.52, Johnson53
and Peprah et al.54
to the successful adoption of AI-driven healthcare solutions53. It is important to have a specially designed
operating system that is user-friendly and adapted for local rural health workers to effectively utilize
AI-driven healthcare solutions. Additionally, there is a need for specific investment in ICT infrastructure
development for rural health centres to support this52.
Also, the study conducted in Ghana demonstrated that despite limited computer knowledge, rural care
providers exhibited positive attitudes toward technology54. This finding focuses on the readiness of
healthcare professionals to embrace technological solutions, portraying a potential for successful
integration with proper training and support. A study conducted in the rural areas of Northern Uganda,
which experienced post-war conflict, also revealed that health workers lacked confidence, knowledge and
skills in using ICTs. However, health workers had positive opinions ab ou t t he b en ef its th at IC Ts co uld br in g
to health service delivery52. These findings suggest that with the right training and support, healthcare
professionals in rural areas are willing to embrace and use technology for healthcare purposes. The lack
of skilled AI experts in sub-Saharan Africa is a big worry. As a result, many healthcare systems are finding
it difficult to handle the growing demand for services while also dealing with severe shortages of
necessary medications and qualified healthcare personnel37.
EMERGING TRENDS IN AI HEALTHCARE TECHNOLOGIES IN THE SUB-SAHARAN AFRICAN HEALTH
SECTOR
This section brie fly looks at the emerging tre nds in AI healthcare technologies in the African health sector
(Table 3). The current state of integrating AI technologies in the healthcare sector of Africa is still in its
early developmental phase38. Although, Africa is globally positioned at a low rank in AI and its associated
activities55, the significance of its influence on African healthcare systems cannot be undermined.
According to research conducted by Owoyemi et al.5, the application of AI in African healthcare settings
has primarily been limited to a small set of pilot projects and test cases. Nevertheless, there is growing
interest and increased investment in deploying AI technologies to enhance diverse facets of healthcare
delivery within the African context38. Emerging trends in African healthcare AI technologies encompass
a range of advancements including the rapid HIV testing in South Africa in which deep learning algorithms
have been used for rapid HIV testing in rural South Africa56.
This technology enables faster and more accurate diagnosis, leading to timely treatment and care. Also,
autonomous drones for medical supply delivery in Rwanda (Zipline drones) have been deployed to
facilitate rapid delivery of medical supplies to remote areas. This technology reduces delivery time and
enhances access to essential medical provisions57. In Nigeria, a startup named Ubenwa employs machine
learning and signal-processing techniques to enhance the detection of birth asphyxia in low-resource
environments5. This technology aids in the early identification and intervention of better neonatal care.
In Kenya, rural health clinics across the community also use a smartphone-based diagnostic tool
inte grated with AI has been use d to perform c ervic al screening 58. This technology enables early detection
of cervical cancer in women, leading to immediate treatment and improved outcomes.
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Table 3: Emerging trends in AI healthcare technologies in the African health sector
Emerging trends Countries References
Deep learning algorithms for rapid HIV testing South Africa Turbé et al.56
Autonomous drones for medical supply delivery (Zipline drone) Rwanda Ackerman and Strickland57
Improved detection of birth asphyxia Nigeria Owoyemi et al.5
Smartphone-based cervical screening Kenya Manyazewal et al.58
Sophia AI for clinical genomics Morocco, Cameroon Aljurf et al.59
and South Africa
Counterfeit drug detection in Nigeria Nigeria Owoyemi et al.5
In Morocco, Cameroon and South Africa, Sophia, an AI system, has also been incorporated into medical
institutions. It analyzes patient genomic data for clinical genomics and maps disease-causing mutations
in their genomic profiles, thereby enabling precise care59. A group of high school girls from Nigeria
created an application using MIT open-source software to identify counterfeit drugs. This technology has
proven to be a valuable asset for the pharmaceutical industry in Nigeria, as it ensures the safety and
efficacy of medications5. These examples demonstrate the potential of AI technologies to improve
healthcare access, diagnosis, treatment and delivery in Africa, while the current level of integration is still
in i ts na scen t stag es, t here i s gro wing in tere st an d inv es tmen t in deploying these technologies to improve
healthcare systems in the region.
RECOMMENDATIONS FOR OVERCOMING CHALLENGES
The implementation of AI in sub-Saharan Africa is hindered by a variety of obstacles, including limited
access to data, the absence of regulatory frameworks, inadequate infrastructure and networking
connectivity, as well as a scarcity of talent and expertise in advanced AI16. To overcome these challenges,
Owoyemi et al.5 suggested a need to accelerate ongoing improvements in African infrastructure,
particularly in electricity and internet accessibility, which could help in the generation and analysis of data
required for advanced mechanization of processes that have to do wi th pa ti ent ca re . L óp ez et al.60 stressed
the need for AI models to be trained and organized under a robust legal and regulatory framework to
meet the public health system requirements of low and middle-income countries (LMICs). Luo et al.61
fur ther highl ight ed the seco ndary use o f dat a heal th to overc ome barriers to data availability, which could
help the researcher uncover novel insights and advancement in medical science. Finally, Ibeneme et al.15
urged the government and all stakeholders to convene to facilitate the necessary focus on artificial
intelligence and digital health in the advancement of the healthcare sector in Africa.
OPPORTUNITIES FOR COLLABORATION AND PARTNERSHIPS
Artificial Intelligence (AI) possesses the potential to significantly enhance healthcare in Africa, presenting
various opportunities for collaboration and partnerships in this sector. Several noteworthy instances of
partnerships and initiatives include google for startups growth academy, a health program designed for
companies situated in Europe, the Middle East and Africa, this program concentrates on the responsible
advancement of AI solutions in the health and well-being industry. Selected startups engage in tailored
workshops and receive long-term Google mentorship and support62. Another one is the ACET and
Convergence AI partnership in Accra. The objective of the collaboration between the African Center for
Economic Transformation (ACET) and Convergence AI is to propel AI research forward, improve
collaboration, share knowledge, create solutions driven by impact and advocate for responsible AI
development in Africa. By leveraging the expertise of both organizations, the partnership endeavours to
push the boundaries of innovation and address critical challenges confronting Africa63.
Also, Vantage Health Technologies, a non-profit organization focused on public health and development,
is joining forces with Nigeria’s Healt h Systems Strengthening (NHED) to harness the power of AI and
long-term in-country contextual expertise in health advocacy. The p a rtne r ship a ims t o en h anc e heal t hcar e
system performance and attain Universal Health Coverage by 203064.
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Another example involves a partnership between Helix Biogen Institute, a prominent translational
biomedical research center in Nigeria and Univercells, an international life sciences firm headquartered
in Belgium. This collaboration aims to collectively enhance the creation, refinement and manufacturing
of mRNA vaccines using artificial intelligence and bioinformatics tools, with a focus on tackling and
adapting to healthcare challenges worldwide65. The collaboration between LifeBank and Bensh AI
partnership seeks to employ AI to enhance patient outcomes in hospitals throughout Africa. By harnessing
the power of AI, the partnership aims to optimize patient care and streamline hospital operations66. All
these partnerships and initiatives serve as evidence of the increasing interest and potential of AI in
enhancing healthcare outcomes and systems in Africa. By nurturing co ll ab or ati on an d k no wle dg e s ha rin g,
these endeavours can contribute to the development of innovative and impactful AI solutions for the
healthcare challenges facing the continent.
CONCLUSION
The integration of AI in the sub-Saharan African health sector holds promise, varying across countries due
to infrastructure and policy variations. The AI’s transformative impacts in diagnostics, treatment and
healthcare operations address challenges like limited resources a nd acc e ss i bi l it y. E x am p le s in c lu d e m o bi l e-
based diagnostics, malaria prediction and precision medicine. However, barriers such as infrastructure
challenges, data privacy concerns and healthcare workforce training gaps hinder the progress. The
healthcare sector’s challenges, including malaria prevalence, are linked to AI’s potential benefits in
diagnostics. Addressing these gaps is crucial for ensuring ethical deployment and consumption, marking
a paradigm shift in healthcare delivery. Healthcare in sub-Saharan Africa holds potential future
improvement through technology adoption, community health initiatives and increased access to medical
services. Challenges like infrastructure limitations and healthcare inequality need addressing for
sustainable progress. Finally, the government and all stakeholders are urged to convene to facilitate the
necessary focus on AI and digital health in the advancement of the healthcare sector in sub-Saharan
Africa.
SIGNIFICANCE STATEMENT
This study underlines the significance of integrating artificial intelligence across the healthcare systems
in sub-Saharan Africa. It elucidates AI’s transformative potential to address myriad challenges in
healthcare, from disease surveillance to the optimization of treatment, amidst resource constraints.
This study highlights the crucial role of AI in improving healthcare access, diagnosis and delivery
in sub-Saharan Africa through the exam in ation of barriers, emerging trends and recommendations.
The main findings emphasize the urgency of overcoming infrastructure limitations and workforce
gaps and underscore the promising partnerships and collaborations driving AI innovation in African
healthcare.
ACKNOWLEDGEMENT
We are grateful to Helix Biogen Institute, Ogbomoso, Oyo State, Nigeria. Their invaluable technical support
on this project is massive. Their expertise and guidance were instrumental in the successful completion
of our review.
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