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Impact and Challenges of Artificial Intelligence Integration in the African Health Sector: A Review

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  • Independent Researcher

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 and barriers like infrastructure and unequal access 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.
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
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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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... The question of AI policy readiness and development [16][17][18][19][20] have seen much attention recently, with some common themes being the need for robust data infrastructure, the importance of context-specific AI readiness assessments, and the critical role of international collaboration and capacity building in ensuring equitable AI development across the African continent. Better understanding challenges and solutions for AI in African health systems has also been a major point of focus, cutting across a range of topics such as addressing health inequities [21], enhancing existing healthcare resources and infrastructure [22][23][24], and improving healthcare accessibility [25][26][27], especially for under-resourced rural areas [28,29]. In a related vein, there is also a growing interest in the prospect of algorithmic colonization, where imported AI technologies from the Global North fail to align with local realities and even contribute to the perpetuation of Global South disparities [30][31][32]. ...
... indicating that they saw some connection between AI and colonialism ( Fig.1). In terms of benefits, making tasks faster and easier was the most dominant response -"AI presents the opportunity to make life easier, quicker and generally better by the elimination of human errors" [Ghana, [21][22][23][24][25][26][27][28][29]. Most concerns revolved around access to knowledge of AI-"Most Africans do not know much about new technologies and this will greatly affect the use of AI in my country" [Nigeria, [30][31][32][33][34][35][36][37][38][39] , and job loss "It means job losses. ...
... To mitigate this, participants suggest creating AI systems that are specifically designed for the African context, using locally sourced and representative data. For P143 [21][22][23][24][25][26][27][28][29]Zimbabwe,Medical Doctor], "If we want to use ML significantly in Africa, we need to create datasets that are developed from Africa. A fair AI system . . . ...
Preprint
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Artificial Intelligence (AI) for health has the potential to significantly change and improve healthcare. However in most African countries, identifying culturally and contextually attuned approaches for deploying these solutions is not well understood. To bridge this gap, we conduct a qualitative study to investigate the best practices, fairness indicators, and potential biases to mitigate when deploying AI for health in African countries, as well as explore opportunities where artificial intelligence could make a positive impact in health. We used a mixed methods approach combining in-depth interviews (IDIs) and surveys. We conduct 1.5-2 hour long IDIs with 50 experts in health, policy, and AI across 17 countries, and through an inductive approach we conduct a qualitative thematic analysis on expert IDI responses. We administer a blinded 30-minute survey with case studies to 672 general population participants across 5 countries in Africa and analyze responses on quantitative scales, statistically comparing responses by country, age, gender, and level of familiarity with AI. We thematically summarize open-ended responses from surveys. Our results find generally positive attitudes, high levels of trust, accompanied by moderate levels of concern among general population participants for AI usage for health in Africa. This contrasts with expert responses, where major themes revolved around trust/mistrust, ethical concerns, and systemic barriers to integration, among others. This work presents the first-of-its-kind qualitative research study of the potential of AI for health in Africa from an algorithmic fairness angle, with perspectives from both experts and the general population. We hope that this work guides policymakers and drives home the need for further research and the inclusion of general population perspectives in decision-making around AI usage.
... The benefits of implementing AI in cardiovascular disease prevention and care in Africa are obvious but introducing and implementing them poses challenges, including inadequate infrastructure and data processing and storage facilities, as well as understanding, trust, and acceptance of such applications [26]. Socioeconomic disparities also play a crucial role, as significant disparities in healthcare access and quality exist across different socioeconomic groups in Africa, and the limited healthcare workforce and expertise in AI and data science in many African countries further exacerbate these challenges [26,27]. ...
... Many regions lack the necessary electricity supply and internet access for the reliable implementation of largescale AI projects. Moreover, access to high-quality locally generated data sets is required to improve on the available tools; however, there is a significant lack of digitized health data and organized collection and collation, so most data sets are collected and stored outside Africa, which limits their use in tackling healthcare issues specific for African people [27]. Also, AI research is incredibly expensive and time-consuming, and while CVDs are of public health concern, more pressing issues like infectious diseases (HIV, TB), neglected tropical diseases and malaria receive far more attention and by default, significantly more funding [26,27]. ...
... Moreover, access to high-quality locally generated data sets is required to improve on the available tools; however, there is a significant lack of digitized health data and organized collection and collation, so most data sets are collected and stored outside Africa, which limits their use in tackling healthcare issues specific for African people [27]. Also, AI research is incredibly expensive and time-consuming, and while CVDs are of public health concern, more pressing issues like infectious diseases (HIV, TB), neglected tropical diseases and malaria receive far more attention and by default, significantly more funding [26,27]. ...
Article
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Background Cardiovascular diseases (CVDs), a significant global health concern, are responsible for 13% of all deaths particularly in Africa, where they contribute substantially to the global disease burden, taking several millions of lives globally and annually. Despite advancements in healthcare, the burden of CVDs continues to rise steadily. This comprehensive review critically examines the intersection of artificial intelligence (AI) and cardiovascular disease (CVD) management in Africa. Drawing on a diverse gamut of scholarly literature and empirical evidence, the review assesses the prevalence, impact, and challenges of CVDs in the African context. Main body The review highlights the potential of AI technologies to revolutionize CVD care, offering insights into its applications in diagnosis, treatment optimization, and remote patient monitoring. It explores existing literature sourced from databases like PUBMED, Scopus and Google Scholar about the current state of AI implementation in African healthcare systems, which are majorly resource-constrained, discussing successes, limitations, and future prospects. The work includes the prevalence and impact of CVDs in Africa, noting the significant public health burden and economic implications. Current challenges in addressing CVDs are outlined, focusing on resource constraints, healthcare system challenges, and socioeconomic factors. Our review takes a dive into AI’s role in healthcare, emphasizing its capabilities in disease diagnosis, treatment optimization, and patient monitoring, and presents current applications and case studies of AI in African cardiovascular healthcare. It also addresses the challenges and limitations of implementing AI in this context, such as inadequate infrastructure, lack of high-quality data, and the need for regulatory frameworks. Conclusion Our review emphasizes the urgent need for collaborative efforts among policymakers, healthcare providers, and researchers to overcome barriers to AI integration and ensure equitable access to innovative healthcare solutions. By fetching existing research and offering practical recommendations, this review contributes to the academic discourse on AI-driven healthcare interventions in Africa, offering an understanding of the opportunities and challenges in leveraging technology to address pressing public health concerns. It calls for increased research, investment, and collaboration to harness AI’s full potential in transforming cardiovascular healthcare in Africa.
... HIV more efficiently than traditional methods. 53 These tools use machine learning algorithms to analyze images or diagnostic tests quickly, providing immediate results to healthcare providers and patients in areas where medical labs and specialists are scarce. 53 Similarly, in remote regions of Asia and the Western Pacific, digital health technologies have significantly enhanced the management of cardiometabolic diseases. ...
... 53 These tools use machine learning algorithms to analyze images or diagnostic tests quickly, providing immediate results to healthcare providers and patients in areas where medical labs and specialists are scarce. 53 Similarly, in remote regions of Asia and the Western Pacific, digital health technologies have significantly enhanced the management of cardiometabolic diseases. AI and other digital tools have been integrated into health systems to support the prevention, diagnosis, and management of these diseases. ...
Article
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This review explores the integration of artificial intelligence (AI) in diagnostic medicine, highlighting its transformative impact on various medical specialties, including radiology, pathology, and patient data management. AI significantly enhances diagnostic accuracy, automates routine tasks, and optimizes healthcare resources, facilitating a shift towards more personalized and preventive medicine. It enables advanced capabilities in medical imaging and predictive diagnostics, improving early disease detection and patient care management. Furthermore, AI’s integration in diagnostic medicine is reshaping economic and global healthcare landscapes by reducing costs, enhancing service accessibility, and driving substantial market growth. These advancements extend AI’s benefits across geographical boundaries, democratizing healthcare and standardizing care across diverse healthcare systems, thereby promising to transform the economic landscape of global health services. Despite its benefits, the integration of AI into mainstream medical practice faces challenges, including ethical concerns about data privacy and the potential biases of algorithms. Addressing these issues requires robust ethical guidelines, transparent practices, and collaborative efforts among stakeholders in technology and medicine. The evolution of AI promises substantial advancements in healthcare efficiency and patient outcomes, contingent on successfully overcoming these technical and practical hurdles.
... This observation is consistent with studies by Oladipo et al. (2024), who found that inadequate infrastructure, particularly in rural areas, poses a significant challenge to deploying AI technologies in health care across Africa. Furthermore, concerns about data privacy and trust in AI systems were raised, with participants emphasising the need for transparent, ethical frameworks to build confidence in AI-driven decisions (Cheong, 2024). ...
... These insights align with Chew and Achananuparp (2022), who found that AI's perceived usefulness in health care significantly increases adoption rates. However, challenges such as inadequate infrastructure, data privacy concerns and trust in AI remain barriers, reflecting findings by Oladipo et al. (2024) on AI adoption in resourceconstrained settings. Addressing these barriers is crucial for fostering widespread AI adoption in Tanzanian mental health care, consistent with TAM's focus on perceived ease of use and trust. ...
Article
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Purpose This study aims to explore how artificial intelligence (AI) can enhance mental health care in Tanzania, focusing on its potential to enhance mental health services and address challenges in a low-resource setting. Design/methodology/approach A qualitative case study approach was used, with data collected through semi-structured interviews and focus group discussions involving key stakeholders in mental health and AI, including policymakers, technical experts, health-care providers and patient advocacy groups. Thematic analysis was used to identify key themes related to the opportunities and barriers to AI integration in mental health care. Findings This study identified several benefits of AI in mental health care, including improved diagnostic accuracy, personalised treatment and the potential for real-time monitoring of patients. However, significant barriers to AI adoption remain, such as infrastructure limitations, data privacy concerns and the need for training and resources to effectively integrate AI into mental health services. Originality/value This study contributes to the growing literature on AI in health care by focusing on its application in mental health care in Tanzania, a low-resource setting. The research provides valuable insights into how AI can bridge gaps in mental health service delivery, particularly in underserved regions, while highlighting the challenges that must be addressed for successful implementation.
... Key issues include regulating AI algorithms, ensuring accountability for AI-driven decisions, addressing algorithmic bias, and protecting data privacy. Existing frameworks, such as the National Health Act (NHA) and Nigerian Data Protection Act (NDPA), provide a foundation but require strengthening to address these challenges [58,66]. Integrating drones into healthcare delivery offers potential solutions to logistical challenges in remote areas, where poor infrastructure limits traditional logistics. ...
Article
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Enhancing primary healthcare delivery in Nigeria by adopting advanced technologies holds substantial promise for improving healthcare accessibility, quality, and efficiency. Primary healthcare focuses on community-based, essential care that is practical, socially acceptable, and affordable. Despite efforts to improve healthcare delivery, challenges persist, particularly in rural and underserved areas. The aim of this study was to explore the transformative potential of technologies such as telemedicine, Electronic Health Records (EHRs), Health Information Systems (HIS), Artificial Intelligence (AI), and medical drones in addressing these challenges. Telemedicine facilitates access to healthcare in remote areas by overcoming geographical barriers. EHRs streamline administrative and clinical processes, enhancing patient care and safety. HIS improves data management, patient safety, and provider communication. AI revolutionizes diagnostics, treatment personalization, and operational efficiency. Medical drones offer innovative solutions for delivering medical supplies to remote locations. The paper also addresses the challenges associated with these technologies, including infrastructure limitations, regulatory issues, and data privacy concerns. Recommendations include investing in infrastructure, developing regulatory frameworks, building capacity, fostering public-private partnerships, engaging communities, and implementing robust data security measures. By addressing these recommendations, Nigeria can leverage advanced technologies to enhance healthcare delivery and achieve better health outcomes. Keywords: Primary health care, telemedicine, Health Information Systems (HIS), Artificial Intelligence (AI), medical drones
... Notable examples include AI-powered platforms for diagnostics, such as Ubenwa, which uses machine learning to detect birth asphyxia from infant cries. Although most AI initiatives in Nigeria are still nascent, they demonstrate the potential of technology to address healthcare challenges, including unplanned pregnancies among adolescents [36,37] . Globally, AI has been successfully implemented in reproductive health interventions, providing valuable case studies for replication in Nigeria [38,39] . ...
Article
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Unplanned pregnancies among adolescents in Nigeria present a significant public health challenge, with far-reaching consequences for health, education, and socioeconomic development. These pregnancies are driven by a complex interplay of factors, including limited access to comprehensive sexual education, inadequate healthcare infrastructure, cultural stigmas, and socioeconomic disparities. Addressing this issue requires innovative and scalable solutions that overcome traditional barriers. Artificial Intelligence (AI) has emerged as a transformative tool in healthcare, offering opportunities to enhance education, access to services, and targeted interventions. This review explores the potential applications of AI in preventing unplanned pregnancies among Nigerian adolescents. It highlights AI-driven tools such as chatbots for personalized health education, predictive analytics for identifying at-risk populations, and mobile health platforms for improving access to contraceptive information and services. Case studies from Nigeria and other countries illustrate the effectiveness of these interventions while emphasizing lessons learned from pilot programs. The study recommends a comprehensive approach to AI implementation, including establishing ethical guidelines, capacity building for healthcare providers, and equity-focused deployment strategies to ensure accessibility for marginalized populations. By leveraging AI, Nigeria can address critical gaps in adolescent reproductive health, reduce unplanned pregnancies, and improve health outcomes, contributing to broader developmental goals.
... This gap may stem from the limited access to advanced technologies and lower investments in health informatics [65]. According to Oladipo et al. [66], regions with weaker digital infrastructure and fewer research funding opportunities may struggle to keep pace with rapid advancements in AI and ML applications in healthcare. Bridging this gap will require dedicated efforts to enhance access to ML technologies and improve research capacity in underrepresented regions. ...
Article
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According to the World Health Organization, chronic illnesses account for over 70% of deaths globally, underscoring the need for effective health risk assessment (HRA). While machine learning (ML) has shown potential in enhancing HRA, no systematic review has explored its application in general health risk assessments. Existing reviews typically focus on specific conditions. This paper reviews published articles that utilize ML for HRA, and it aims to identify the model development methods. A systematic review following Tranfield et al.'s three-stage approach was conducted, and it adhered to the PRISMA protocol. The literature was sourced from five databases, including PubMed. Of the included articles, 42% (11/26) addressed general health risks. Secondary data sources were most common (14/26, 53.85%), while primary data were used in eleven studies, with nine (81.81%) using data from a specific population. Random forest was the most popular algorithm, which was used in nine studies (34.62%). Notably, twelve studies implemented multiple algorithms, while seven studies incorporated model interpretability techniques. Although these studies have shown promise in addressing digital health inequities, more research is needed to include diverse sample populations, particularly from underserved communities, to enhance the generalizability of existing models. Furthermore, model interpretability should be prioritized to ensure transparent, trustworthy, and broadly applicable healthcare solutions.
... These regulations mandate that data be used only for its intended purpose, with patients' explicit consent, and that it be protected against unauthorized access. Encryption is a fundamental security measure for protecting patient data [9]. Data encryption involves converting sensitive information into a coded format that can only be decrypted by authorized individuals. ...
Research
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Artificial Intelligence (AI) is poised to revolutionize healthcare in Africa, particularly in the realm of cancer detection, where access to timely and accurate diagnostics is often limited. By harnessing advanced AI technologies, such as machine learning and deep learning algorithms, there is a significant opportunity to improve cancer detection rates and outcomes across the continent. AI-driven tools are capable of analyzing medical images, such as X-rays, MRIs, and CT scans, with high precision, enabling the early identification of cancerous lesions that might be missed by human observers. This capability is especially valuable in Africa, where there is a shortage of specialized radiologists and oncologists. AI systems can support healthcare professionals by providing automated and accurate readings of diagnostic images, thereby accelerating the diagnostic process and reducing the burden on healthcare facilities. Furthermore, AI can enhance cancer detection through predictive analytics and pattern recognition. By analyzing vast amounts of data from electronic health records, AI algorithms can identify patterns and risk factors associated with different types of cancer. This predictive capability allows for earlier intervention and personalized treatment plans tailored to individual patient profiles, which can be crucial in regions with limited resources. The implementation of AI in cancer detection also promises to bridge the gap between urban and rural healthcare services. Mobile and remote AI solutions can facilitate access to advanced diagnostic tools in underserved areas, reducing disparities in healthcare delivery and ensuring that more patients receive timely and accurate diagnoses. However, the successful integration of AI in cancer detection in Africa requires addressing challenges such as data privacy, algorithmic bias, and infrastructure limitations. Ensuring that AI systems are trained on diverse and representative datasets, and that they are implemented with robust security measures, is essential for achieving equitable and effective outcomes.
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Patient care and medical research are changing as a result of the application of artificial intelligence (AI) in healthcare. To fully realize the potential of AI technology, collaborative relationships between AI developers and healthcare providers are essential. This study examines the advantages and prospects of collaborating with healthcare professionals to improve healthcare outcomes. Personalized medicine, clinical decision support systems, healthcare process optimization, patient engagement, and ethical considerations are just a few of the areas where AI and healthcare practitioners are collaborating. Significant progress can be made by fusing the knowledge of healthcare professionals with AI's powers in data analysis, pattern recognition, and predictive modeling. Advancements in diagnosis and therapy are a major area of collaboration. Healthcare practitioners can gain from enhanced diagnostic precision, early illness identification, and exact treatment planning by integrating AI algorithms with patient data. Enhanced patient outcomes and improved healthcare delivery are the outcomes. The development of personalized medicine techniques is also made possible by collaboration. Healthcare professionals can customize treatment strategies based on unique genetic markers, biomarkers, and clinical factors by utilizing AI algorithms to examine patient data. This collective effort results in improved treatments and treatment outcomes. Clinical decision support system development is facilitated by collaborations between AI and healthcare professionals. By analyzing patient data, medical literature, and clinical recommendations using AI technology, these systems offer real-time guidance to medical personnel. Clinical decision support systems increase the effectiveness of diagnosis, the choice of treatment, and patient safety by strengthening decision-making abilities. In healthcare settings, collaboration also emphasizes process improvement, increasing effectiveness, and resource management. Artificial intelligence (AI) algorithms can examine operational data and patient flow patterns to spot inefficiencies, resulting in the simplification of administrative work, enhanced patient scheduling, and better resource management. Costs are reduced, operational effectiveness is raised, and patient experiences are improved as a result. Patient participation and experience are another facet of partnership. Artificial intelligence-enabled virtual assistants and catboats offer individualized support, respond to patient questions, and deliver health information. These resources improve patient satisfaction, ease of access to healthcare, and patient empowerment in health management. AI and healthcare practitioners working together must take ethical issues and legal compliance very seriously. It is crucial to protect patient privacy, guarantee data security, and abide by ethical standards and regulatory frameworks. Collaborations can improve healthcare results and preserve patient trust by taking these factors into account. AI and healthcare providers working together could change how patients are treated, promote medical research, and enhance patient outcomes. Partnerships that make use of AI technologies and integrate them with healthcare knowledge promote innovation, improve patient engagement, optimize diagnostic and therapeutic procedures, and ensure ethical and legal compliance. AI and healthcare professionals work together continuously to enhance patient outcomes and the standard of care, shaping the future of healthcare delivery.