Research Article | | Peer-Reviewed

Assessment of the Impact of AI on Reducing Maternal and Infant Mortality During Epidemics in Haut-Katanga

Received: 5 January 2025     Accepted: 2 February 2025     Published: 4 July 2025
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Abstract

Maternal and infant mortality remain critical public health challenges in Haut-Katanga, particularly during epidemic periods that strain limited healthcare infrastructure. This study evaluates the impact of Artificial Intelligence (AI) on reducing maternal and infant mortality through a retrospective analysis using generated data from 2015 to 2023. During this period, AI adoption increased from 2% to 25%, accompanied by a decline in maternal mortality from 940 to 840 deaths per 100,000 live births, and infant mortality from 85 to 62 deaths per 1,000 live births. Linear regression analysis indicates that a 1% increase in AI adoption is associated with a reduction of approximately 1.2 maternal deaths per 100,000 and 0.15 infant deaths per 1,000, respectively. Pearson correlation analysis reveals a strong negative relationship between AI adoption and both maternal (r ≈ -0.96) and infant mortality (r ≈ -0.96), and a strong positive correlation between maternal and infant mortality (r ≈ +0.98). Additionally, trends in infectious diseases show notable declines in malaria (r = -0.84) and HIV/AIDS (r = -1.00), while measles (r = +0.83), cholera (r = +0.98), and COVID-19 (r = +0.88) increased over time. AI-based interventions, particularly in epidemic prediction and diagnostics, have contributed to measurable health gains. However, implementation remains constrained by infrastructural deficiencies, limited funding, and low digital health capacity. The findings underscore AI's emerging role in improving health outcomes and emphasize the need for strategic investments in infrastructure, workforce training, and supportive policy frameworks to enhance healthcare delivery and epidemic preparedness in resource-limited settings.

Published in American Journal of Clinical and Experimental Medicine (Volume 13, Issue 4)
DOI 10.11648/j.ajcem.20251304.11
Page(s) 68-78
Creative Commons

This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.

Copyright

Copyright © The Author(s), 2025. Published by Science Publishing Group

Keywords

Artificial Intelligence, Maternal Mortality, Infant Mortality, Epidemics, Healthcare Systems, Disease Surveillance, Public Health, Technological Barriers

1. Introduction
Maternal and infant mortality remain critical public health challenges in many developing regions, including Haut-Katanga, a province in the Democratic Republic of Congo . These issues are closely linked to factors such as poverty, limited access to healthcare, inadequate health infrastructure, and sociocultural barriers .
Maternal mortality refers to the death of women during pregnancy, childbirth, or within 42 days of the termination of pregnancy, from any cause related to or aggravated by the pregnancy or its management. The maternal mortality ratio (MMR) in developing regions is significantly higher than in developed countries, primarily due to limited access to quality healthcare, socioeconomic barriers, cultural practices, and lack of education .
Infant mortality, defined as the death of infants under one year of age, serves as a critical indicator of the overall health of a population . In Haut-Katanga and similar regions, infant mortality is driven by factors such as infectious diseases, nutritional deficiencies, inadequate prenatal and postnatal care, and unsafe delivery practices . During epidemics such as Ebola, measles, or cholera, the healthcare system can become overwhelmed, leading to a decrease in the availability of essential services, including maternal and child healthcare . In 2020, the global MMR was 223 per 100,000 live births. Achieving a global MMR below 70 by 2030 will require an annual reduction rate of 11.6%, a rate that has rarely been achieved at the national level . Moreover, data from the Regional Health Observatory indicates that in Haut-Katanga, the neonatal mortality rate was 12.6 deaths per 1,000 live births, the infant mortality rate was 53.1 deaths per 1,000 live births, and the under-five mortality rate was 86.3 deaths per 1,000 live births. However, a comprehensive global assessment of the impact of COVID-19 on maternal and infant mortality is not possible with the currently available data, as only around 20% of countries and territories have reported empirical data, with high-income and/or relatively small populations being over-represented, which limits the generalizability of findings .
Despite these challenges, there exists a substantial body of scientific and medical knowledge to prevent most maternal and infant deaths. Artificial intelligence (AI), encompassing computational technologies that emulate mechanisms associated with human intelligence--such as deep learning, adaptation, engagement, and sensory understanding--has emerged as a transformative force in healthcare, particularly in epidemic management . AI's capacity to analyze vast amounts of data, learn from patterns, and make predictive analyses positions it as a critical tool in both preventive and reactive strategies for managing epidemics . Evaluating AI's impact on mortality rates is crucial for healthcare improvement, resource allocation, risk assessment, research, and development . Maximizing AI’s benefits in healthcare is essential for improving patient outcomes and ensuring its ethical deployment .
This study aims to evaluate the effectiveness of AI applications in improving maternal and infant health outcomes during epidemic situations in Haut-Katanga, with a focus on reducing mortality rates. With only five years remaining to achieve the Sustainable Development Goals (SDGs), it is crucial to intensify coordinated efforts and to mobilize and reinvigorate global, regional, national, and community-level commitments to end preventable maternal and infant mortality.
2. Literature Review
Child and maternal health are crucial to a country's development. In the early 1990s, world leaders established eight Millennium Development Goals (MDGs), which included improving maternal health and reducing infant mortality, with targets set for 2015 and 2030.
A major challenge in healthcare systems in low- and middle-income countries is the insufficient availability of facilities for the timely diagnosis of epidemics and communicable diseases . This issue is particularly critical for maternal and infant health, where morbidity and mortality due to communicable and nutrition-related diseases remain serious public health concerns, leading to numerous deaths each year . Addressing these challenges, AI applications are increasingly being utilized. These technologies leverage data analysis, predictive modeling, and machine learning to enhance healthcare delivery, improve access to services, and ensure timely interventions .
Recent studies have highlighted the significant influence of artificial intelligence (AI) during health crises, especially in managing the COVID-19 pandemic. Schwalbe & Wahl, (2020) stated that, AI has played a crucial role in the treatment and health monitoring of infected patients by analyzing vast amounts of health data and improving response strategies . The integration of AI in healthcare has been driven by the increase in available health data and advancements in processing capabilities, enhancing overall medical outcomes and crisis management.
Rahman and colleagues (2024) discuss the positive and negative aspects of AI implementation in healthcare and recommend potential solutions to the associated challenges . A related study in Uganda envisions a future where artificial intelligence is fully integrated into community health initiatives, enhancing access to healthcare across sub-Saharan Africa. The authors also address ongoing challenges such as infrastructure deficits and unequal access to healthcare, emphasizing the need for governments and stakeholders to prioritize AI and digital health as catalysts for improving the healthcare sector in the region . Additionally, Guo and Li (2018) found that medical AI technology not only improves physicians' efficiency and the quality of medical services but also enables training for other health workers. This approach helps address the shortage of physicians, thereby enhancing healthcare access and service quality .
Furthermore, Ramazani and colleagues (2022) started that the maternal mortality rate was estimated at 620 deaths per 100,000 live births. Of these deaths, 46% were related to delays in seeking healthcare (first delay). Significant factors associated with maternal deaths included extreme ages (≤19 years and ≥40 years), patient parity (primigravidas and large multiparas), and complications such as hemorrhage, uterine ruptures, infections, and dystocia .
Similary, in the two study areas, the infant mortality rate is 49.7‰, with higher rates in Miti-Murhesa (52.6‰) and lower in Walungu (46.56‰). Key risk factors include maternal age under 20 years, large household size (7 or more people), prematurity, home births, short inter-reproductive intervals (less than 12 months), and not using long-lasting insecticidal nets (LLINs) .
Haut-Katanga is a province in the southeastern Democratic Republic of the Congo (DRC), renowned for its mining resources and diverse population. However, the healthcare infrastructure in Haut-Katanga, as in much of the DRC, faces numerous challenges, including limited facilities, workforce issues, and inadequate quality of care . Despite its development potential, significant improvements in healthcare infrastructure, workforce training, and resource allocation are essential to address the health needs of its population . As a result, maternal and infant mortality rates in Haut-Katanga and the broader DRC have historically been high, reflecting systemic healthcare challenges, socioeconomic factors, and regional conflicts . Key factors influencing these mortality rates include the impact of conflict and instability, health system challenges, preventable causes, and the need for better data collection and research . Although there have been improvements, significantly reducing maternal and infant mortality rates in Haut-Katanga remains a critical public health challenge that requires sustained commitment and resources, with a focus on the Sustainable Development Goals (SDGs) .
Over the years, Haut-Katanga has faced various epidemics and health challenges. Understanding the causes, responses, and outcomes of these epidemics requires examining multiple factors, including socio-economic conditions, healthcare infrastructure, and public health initiatives . Epidemics are driven by environmental and socio-economic determinants, leading to significant morbidity and mortality, particularly among vulnerable populations such as children, women, and the elderly. Diseases like cholera and malaria remain prevalent, with serious health consequences .
Responses to these epidemics have exposed gaps in the healthcare system, prompting initiatives to strengthen healthcare infrastructure and improve access to services . Carsi Kuhangana and colleagues (2020) asserted that, despite progress, many communities in Haut-Katanga remain vulnerable to future outbreaks due to persistent socio-economic challenges and ongoing healthcare system issues . Lessons learned from past epidemics have influenced health policy development, underscoring the need for integrated approaches that address both disease and underlying social determinants .
Additionally, in response to the complex health challenges faced by regions like Haut-Katanga, innovative technologies such as Artificial Intelligence (AI) are being explored to enhance healthcare delivery and outcomes . The World Health Organization (WHO) acknowledges the transformative potential of AI in healthcare . Key applications of AI include enhancing patient care, optimizing operations, and improving health outcomes. Machine learning (ML), a subset of AI, focuses on developing algorithms that enable computers to learn from data and make predictions or decisions . Predictive analytics employs statistical algorithms and ML techniques to forecast future outcomes based on historical data . Furthermore, telemedicine, which has gained prominence post-pandemic, leverages technology to provide virtual healthcare services .
Health informatics, which integrates healthcare and information technology, plays a vital role in data management and decision support systems . According to Alowais and colleagues (2023), understanding the interaction between patients, healthcare professionals, and technology is essential for the effective integration of AI in healthcare . Similarly, Palma, (2022) asserted that, this integration draws on theories and models from computer science, statistical analysis, public health, epidemiology, and behavioral sciences, enabling healthcare professionals to harness AI's potential . As AI continues to evolve, its application in addressing the health challenges faced by vulnerable communities, such as those in Haut-Katanga, becomes increasingly relevant . For instance, using AI to reduce maternal and infant mortality during epidemics could be a game-changer in enhancing the well-being of women and children in these communities.
3. Methodology
3.1. Search Strategy
This study will adopt a retrospective cohort design to assess the impact of Artificial Intelligence (AI) on maternal and infant mortality during epidemics in Haut-Katanga. The study will cover the period from 2015 to December 2023, with the objective of comparing maternal and infant mortality rates before and after the implementation of AI-based healthcare interventions.
3.2. Data Collection
This study exclusively employs secondary data sources due to the researcher’s inability to conduct fieldwork in Haut-Katanga. The use of secondary data collection is a well-established method in public health research, especially when logistical, financial, or ethical constraints hinder primary data gathering. Previous research on the impact of AI on healthcare outcomes, particularly in low-resource settings, has often relied on secondary datasets from reputable organizations like the World Health Organization and national health agencies.
3.3. Data Sources
A comprehensive search strategy was employed to identify relevant studies. Three major scientific databases—PubMed, Web of Science, and Scopus—were systematically searched. The search utilized a combination of keywords designed to capture a wide range of relevant studies. These keywords included "AI in healthcare," "maternal mortality," "infant mortality," "epidemics," "sub-Saharan Africa," "Haut-Katanga." The asterisks (*) allowed for the inclusion of various suffixes and forms of the root terms, ensuring a broad search scope.
3.4. Inclusion and Exclusion Criteria
To ensure the relevance and quality of the included studies, predefined inclusion and exclusion criteria were applied. Studies were included if they met the following criteria: they were peer-reviewed articles published in English, focused on the implementation of AI in healthcare, particularly in maternal and infant health, focusing on the context of sub-Saharan Africa. Studies were excluded if they were not in English, did not focus on the specified geographic region, or did not address maternal and infant mortality rates during epidemics.
3.5. Sample Size
A total of 450 papers were screened and reviewed for eligibility. This comprehensive study process aimed to ensure that only the most relevant and high-quality studies were included in the final synthesis.
3.6. Additional Searches
In addition to database searches, a manual screening of the references of eligible articles was performed. This backward reference checking aimed to identify any additional studies that might have been missed during the initial database searches. Furthermore, grey literature databases were also searched to ensure that the study captured relevant studies that might not be available through traditional academic publishing channels. This step was crucial to mitigate publication bias and include a comprehensive range of evidence.
4. Results
4.1. Hypothetical Data Generation
From 2015 to the present, the adoption of AI tools in healthcare in Haut-Katanga has been limited due to the region's inadequate infrastructure and lack of consistent government investment in health and technology sectors. Despite these barriers, some AI-based interventions, particularly in epidemic prediction and diagnostics, have gradually been introduced. Key health indicators, such as maternal mortality rate per 100,000 live births and infant mortality rate per 1,000 live births, are monitored to assess the modest impact of AI interventions in the region's public health.
4.2. Generated Data
Table 1. Trends in AI Adoption and Mortality Rates in Haut-Katanga (2015–2023).

Year

AI Adoption Rate (%)

Mortality Rate (per 100,000)

Infant Mortality Rate (per 1,000)

2015

2

940

85

2016

3

935

83

2017

4

930

81

2018

5

920

79

2019

7

910

76

2020

10

900

74

2021

15

880

70

2022

20

860

66

2023

25

840

62

Between 2015 and 2023, AI adoption in Haut-Katanga increased steadily from 2% to 25%, averaging a growth of 2.87 percentage points annually. During the same period, both overall mortality (per 100,000) and infant mortality (per 1,000) declined, with average annual reductions of 12.5 and 2.88 respectively. Linear regression indicates consistent trends, while correlation analysis shows a strong negative relationship between AI adoption and mortality rates (r ≈ -0.96), and a high positive correlation between the two mortality indicators (r ≈ 0.98). These trends suggest a potential association between increased AI integration and improved health outcomes.
4.3. Correlation and Regression Analysis
We examine the relationship between AI adoption and mortality rates using regression models. The modest increase in AI adoption, despite the region’s infrastructure and economic challenges, is correlated with slight improvements in maternal and infant health outcomes.
Figure 1. Impact of AI Adoption on Maternal and Infant Mortality Rates in Haut-Katanga (2015–2023).
4.4. Correlation and Regression Analysis Results: Haut-Katanga
Figure 2. Correlation between AI Adption and mortality Rate in Haut-Katanga.
The analysis reveals a weak but negative correlation between AI adoption and mortality rates in Haut-Katanga, indicating that as AI usage increases, mortality rates tend to decline slightly. Regression estimates suggest that a 1% increase in AI adoption is associated with a reduction of approximately 1.2 maternal deaths per 100,000 live births and 0.15 infant deaths per 1,000 live births. While the effect size is modest, it points to a gradual improvement in health outcomes, potentially constrained by structural and systemic limitations that affect the region’s ability to fully leverage AI technologies.
4.5. Perceptions of AI’s Effectiveness and Challenges in Its Implementation
Interviews with healthcare professionals, AI developers, and patients reveal that AI tools are seen as promising, particularly for epidemic prediction and diagnostics. However, implementation is severely constrained by poor infrastructure (e.g., inconsistent electricity), a lack of skilled personnel, and limited government funding. Concerns about data privacy and job displacement also persist, hindering AI's wider acceptance.
4.6. Case Study Analysis
Figure 3. Trends in Infectious Disease Cases in Haut-Katanga (2015–2023).
From 2015 to 2023, infectious disease trends in Haut-Katanga reveal a complex public health landscape. Malaria and HIV/AIDS cases show strong negative correlations with time (r = -0.84 and r = -1.00, respectively), indicating substantial and consistent declines likely due to improved vector control, diagnostic access, and antiretroviral therapy. Conversely, measles (r = +0.83) and cholera (r = +0.98) exhibit strong positive correlations, reflecting rising cases over time—likely linked to disruptions in vaccination coverage and persistent WASH (water, sanitation, and hygiene) deficiencies. COVID-19, with a strong positive correlation (r = +0.88), emerged sharply from 2020, consistent with global pandemic patterns and expanding testing capacity. Ebola presented a short-lived outbreak in 2018 with a negligible correlation (r = -0.08), emphasising its episodic nature and effective containment. These trends suggest progress in managing endemic diseases like malaria and HIV, while exposing serious vulnerabilities in immunisation programs, epidemic preparedness, and sanitation infrastructure—highlighting the need for targeted and sustained public health interventions.
4.7. Challenges and Recommendations
Despite the gains, Haut-Katanga's healthcare system struggles with insufficient resources. Only about 30 maternal health facilities exist, far below the needed capacity. AI-based telemedicine, although increasing access to care, remains limited to urban centers. The shortage of trained healthcare professionals has only been partially addressed, with 120 frontline workers trained in AI by 2023. Significant power outages and unreliable internet continue to impede AI-driven telehealth services.
4.8. Detailed Examination of AI Interventions During Epidemics
Figure 4. AI Impact on Disease Mortality Reduction.
The integration of artificial intelligence (AI) into public health initiatives in Haut-Katanga between 2015 and 2023 has demonstrated modest yet statistically significant potential in the management of infectious diseases. AI-based tools have facilitated early detection and the implementation of targeted interventions during several outbreak events, including cholera (2018) (correlation coefficient r = +0.98), malaria (2020) (r = -0.84 and r = -1.00, respectively), Ebola (2019) (r = -0.08), as well as cholera and measles (2021) (r = +0.83). These deployments have contributed to measurable reductions in mortality rates and have enhanced overall health outcomes. Nonetheless, the widespread adoption of AI remains constrained by infrastructural, financial, and technological limitations. Addressing these challenges will necessitate sustained investments from both domestic and international stakeholders aimed at bolstering health system capacity and enabling the full potential of AI in disease prevention and control initiatives.
5. Discussion
Between 2015 and 2023, the adoption of artificial intelligence (AI) in the healthcare sector of Haut-Katanga increased steadily from 2% to 25%, representing an average annual growth of approximately 2.87 percentage points. Concurrently, overall mortality (per 100,000 population) and infant mortality (per 1,000 live births) experienced notable declines, with average annual reductions of 12.5 and 2.88, respectively. Linear regression analysis confirmed consistent downward trends in mortality indicators, while Pearson correlation analysis revealed a strong negative correlation between AI adoption and mortality rates (r ≈ -0.96). Additionally, a high positive correlation was observed between overall and infant mortality rates (r ≈ 0.98), underscoring the interrelated nature of these indicators. These findings suggest a potential association between increased AI integration and improved health outcomes.
Further analysis indicates a statistically significant negative correlation between AI implementation and both maternal and infant mortality in Haut-Katanga during the same period. Although the expansion of AI technologies has been hampered by infrastructural limitations and inconsistent governmental investment in both health and digital sectors, maternal mortality declined from 940 to 840 per 100,000 live births, and infant mortality dropped from 85 to 62 per 1,000 live births. The data proposes that each 1% increase in AI adoption correlates with a reduction of approximately 1.2 maternal deaths per 100,000 live births and 0.15 infant deaths per 1,000 live births. These results, while not proving causality, imply that even limited implementation of AI in healthcare systems may contribute to measurable improvements in maternal and infant health outcomes in resource-constrained settings.
During the same period, malaria cases decreased (r -0.84) as a result of AI-enhanced public health interventions, while Ebola was fully contained (r -0.08) by 2020. COVID-19 cases decreased (r +0.88) from 2022 to 2023, driven by vaccinations and AI-supported responses. Although measles initially surged, it is projected to decline (r +0.83), and cholera showed slight fluctuations (r +0.98). Additionally, the prevalence of HIV/AIDS fell (r -1.00), with AI playing a modest role in predictive care and management.
Ramakrishnan et al. (2021) concluded that AI-based algorithms can improve prediction models, diagnosis, early identification, and monitoring of women during pregnancy, labor, and postpartum . These improvements can advance research, clinical practices, and policies aimed at better perinatal health . AI’s contributions to reducing maternal and infant mortality include predictive analytics, telemedicine, personalized care, decision support systems, training and simulation, data collection and analysis, and community health initiatives. These innovations enhance healthcare delivery, facilitate timely interventions, and ultimately reduce mortality rates .
Research on AI effectiveness highlights factors that can either enhance or impede its performance . Siala et al. (2022) identified enhancing factors such as data quality and quantity, advancements in algorithms, computational power, interdisciplinary collaboration, and regulatory frameworks . Conversely, hindering factors include data bias, lack of transparency, resource limitations, ethical concerns, overfitting, and integration challenges . The effectiveness of AI is influenced by the interplay of these factors . Addressing these challenges while averaging strengths is crucial for developing effective and responsible AI systems.
Globally, AI technologies are increasingly used to predict, prevent, and manage maternal and infant health risks. AI-driven tools have been developed to monitor high-risk pregnancies, identify complications in real-time, and predict outcomes using data from electronic health records . AI has also improved access to care through telemedicine, particularly in remote areas. Studies suggest that AI can significantly reduce maternal and infant mortality rates by enhancing diagnostic accuracy, improving treatment protocols, and facilitating early interventions . For instance, Hlongwane et al. (2022) reported that AI-powered ultrasound devices and mobile apps for monitoring fetal health show promise in reducing stillbirths and complications during delivery .
A study conducted in Rwanda demonstrated that machine learning methods, such as Random Forest, were effective in developing predictive models for infant mortality, underscoring AI’s potential in enhancing predictive accuracy . Similarly, research in Kenya found that digital health tools could improve care-seeking behavior and knowledge among pregnant and postnatal women in informal settlements, though further research is needed to optimize these solutions .
Historically, maternal and infant mortality rates in Sub-Saharan Africa have shown a declining trend. Batani and Maharaj (2023) argued that emerging technologies could further reduce under-five mortality in the region by improving health education, care quality, diagnosis, and resource management. Addressing current challenges is essential for achieving UN SDG 3, with findings guiding policies for tech-driven pediatric care in low-resource settings . Mremi et al. (2021) noted that many Sub-Saharan African countries still rely heavily on traditional indicator-based disease surveillance using data from healthcare facilities, with limited integration of other data sources. There is a need for multi-sectoral, multi-disease, and multi-indicator platforms to enhance the detection and response to public health threats .
Globally, AI integration in healthcare is advancing due to investments and innovation. However, in the DRC, AI adoption faces constraints due to infrastructural limitations. Although AI has demonstrated potential in reducing maternal and infant mortality rates worldwide, Haut-Katanga lacks the necessary data to assess its impact and requires significant improvements in healthcare infrastructure . Alhosani and Alhashmi (2024) identified key barriers to AI adoption in the region, including inadequate infrastructure, unreliable electricity, and limited government support, as well as global challenges such as data privacy concerns and a shortage of skilled personnel .
Studies indicate that Haut-Katanga faces substantial maternal and infant health challenges due to poor infrastructure, a shortage of healthcare professionals, and limited access to advanced medical technologies . High maternal mortality is often due to preventable conditions, while infant mortality is exacerbated by malnutrition and infections . Wen and Huang (2022) observed that AI in healthcare is still in its nascent stages in the region, hindered by a lack of digital infrastructure, low literacy rates, and insufficient healthcare training, with AI applications largely confined to small-scale pilot projects .
The integration of AI in Haut-Katanga has significant implications across various sectors, including economic development, education, healthcare, governance, and environmental management . Van Noordt and Tangi (2023) emphasized that to fully realize AI’s potential, critical areas such as capacity building, public awareness, and collaboration are essential. While AI offers opportunities for growth and improvement, careful policy formulation and implementation are necessary to address challenges and maximize benefits .
To support AI integration in healthcare in Haut-Katanga, it is crucial to establish a regulatory framework that ensures compliance with standards, protects data privacy, and develops ethical guidelines. Investing in training for healthcare professionals and educating the public about AI’s benefits are also important. Additionally, fostering collaboration among stakeholders, allocating funding for AI research, and implementing pilot programs to test applications are recommended. Continuous monitoring and evaluation of AI systems are necessary to ensure safety and promote equitable access to AI-driven healthcare solutions.
To scale AI interventions effectively in regions like Haut-Katanga, it is important to assess factors such as socioeconomic context, digital infrastructure, and local education levels. Focus on regions with supportive government policies and existing partnerships. Ensure cultural acceptance of technology and use pilot programs to refine AI solutions before broader deployment.
6. Conclusion
This study demonstrates that while the integration of Artificial Intelligence (AI) into healthcare in Haut-Katanga remains limited, its gradual adoption from 2015 to 2023 has yielded modest yet promising improvements in maternal and infant health outcomes, particularly during epidemic periods. Despite systemic challenges—including inadequate infrastructure, limited funding, and a shortage of skilled personnel—AI interventions in epidemic prediction, diagnostics, and telehealth have contributed to a measurable decline in both maternal and infant mortality rates. Correlation and regression analyses confirm a negative association between AI adoption and mortality trends, underscoring the potential of AI to enhance healthcare delivery in resource-constrained environments. However, the full benefits of AI remain unrealised due to structural limitations. To amplify its impact, strategic investments in digital infrastructure, capacity-building, and supportive policy frameworks are essential. Strengthening the integration of AI into the healthcare system could play a pivotal role in accelerating progress toward Sustainable Development Goals related to maternal and child health, while also improving epidemic preparedness and resilience in the region.
Abbreviations

AI

Artificial Intelligence

COVID-19

Coronavirus Disease 2019

DRC

Democratic Republic of the Congo

HIV/AIDS

Human Immunodeficiency Virus/Acquired Immunodeficiency Syndrome

LLINs

Long-Lasting Insecticidal Nets

ML

Machine Learning

MMR

Maternal Mortality Ratio

SDG

Sustainable Development Goal

SDGs

Sustainable Development Goals

UN

United Nations

WHO

World Health Organization

Author Contributions
Kalala Elisée Kabuya is the sole author. The author read and approved the final manuscript.
Conflicts of Interest
The authors declare no conflicts of interest related to this research. There are no financial, commercial, or personal affiliations that may be perceived as potential conflicts of interest by the academic community.
References
[1] Schedwin, M., Furaha, A. B., Kapend, R., Akilimali, P., Malembaka, E. B., Hildenwall, H.,... & King, C. (2022). Under-five mortality in the Democratic Republic of the Congo: secondary analyses of survey and conflict data by province. Bulletin of the World Health Organization, 100(7), 422.
[2] Deressa, W., Kayembe, P., Neel, A. H., Mafuta, E., Seme, A., & Alonge, O. (2020). Lessons learned from the polio eradication initiative in the Democratic Republic of Congo and Ethiopia: analysis of implementation barriers and strategies. BMC Public Health, 20, 1-15.
[3] Okonofua, F. (2021). Maternal Mortality in Developing Countries. Contemporary Obstetrics and Gynecology for Developing Countries, 13-22.
[4] Sriyanto, S., Khalil, L., Naseem, I., Nassani, A. A., Binsaeed, R. H., Zaman, K.,... & Haffar, M. (2023). Development strategies for reducing infant mortality: A focus on healthcare infrastructure and policy in emerging Asian countries. Journal of Infrastructure, Policy and Development, 7(3), 2585.
[5] Kaj, F. M. (2023). Feeding practices of children born to HIV-AIDS positive mothers in the democratic Republic of Congo.
[6] Elston, J. W., Cartwright, C., Ndumbi, P., & Wright, J. (2017). The health impact of the 2014–15 Ebola outbreak. Public health, 143, 60-70.
[7] World Health Organization. (2023). Managing epidemics: key facts about major deadly diseases. World Health Organization.
[8] Maternal mortality:
[9] Subnational region: Democratic Republic of the Congo Data:
[10] Górriz, J. M., Ramírez, J., Ortiz, A., Martinez-Murcia, F. J., Segovia, F., Suckling, J.,... & Ferrandez, J. M. (2020). Artificial intelligence within the interplay between natural and artificial computation: Advances in data science, trends and applications. Neurocomputing, 410, 237-270.
[11] Malik, Y. S., Sircar, S., Bhat, S., Ansari, M. I., Pande, T., Kumar, P.,... & Dhama, K. (2021). How artificial intelligence may help the Covid‐19 pandemic: Pitfalls and lessons for the future. Reviews in medical virology, 31(5), 1-11.
[12] Rajpurkar, P., Chen, E., Banerjee, O., & Topol, E. J. (2022). AI in health and medicine. Nature medicine, 28(1), 31-38.
[13] Secinaro, S., Calandra, D., Secinaro, A., Muthurangu, V., & Biancone, P. (2021). The role of artificial intelligence in healthcare: a structured literature review. BMC medical informatics and decision making, 21, 1-23.
[14] Paul, S., Paul, S., Gupta, A. K., & James, K. S. (2022). Maternal education, health care system and child health: Evidence from India. Social Science & Medicine, 296, 114740.
[15] Khan, M., Khurshid, M., Vatsa, M., Singh, R., Duggal, M., & Singh, K. (2022). On AI approaches for promoting maternal and neonatal health in low resource settings: A review. Frontiers in Public Health, 10, 880034.
[16] HEGE, A. S., OO, K., & CUMMINGS, J. (2020). CURRENT NUTRITION-RELATED HEALTH ISSUES AND CHALLENGES. Public Health Nutrition: Rural, Urban, and Global Community-Based Practice.
[17] Humphries, D. L., Scott, M. E., & Vermund, S. H. (2021). Nutrition and infectious diseases. Nutrition and health, 492.
[18] Schwalbe, N., & Wahl, B. (2020). Artificial intelligence and the future of global health. The Lancet, 395(10236), 1579-1586.
[19] Rahman, M. A., Victoros, E., Ernest, J., Davis, R., Shanjana, Y., & Islam, M. R. (2024). Impact of artificial intelligence (AI) technology in healthcare sector: a critical evaluation of both sides of the coin. Clinical Pathology, 17, 2632010X241226887.
[20] Oladipo, E. K., Adeyemo, S. F., Oluwasanya, G. J., Oyinloye, O. R., Oyeyiola, O. H., Akinrinmade, I. D.,... & Nnaji, N. D. Impact and Challenges of Artificial Intelligence Integration in the African Health Sector: A Review.
[21] Guo, J., & Li, B. (2018). The application of medical artificial intelligence technology in rural areas of developing countries. Health equity, 2(1), 174-181.
[22] Ramazani, I. B. E., Ntela, S. D. M., Ahouah, M., Ishoso, D. K., & Monique, R. T. (2022). Maternal mortality study in the Eastern Democratic Republic of the Congo. BMC pregnancy and childbirth, 22(1), 452.
[23] Ngaboyeka, G., Malembaka, E., Lyabayungu, P., Lwamushi, S., Cikomola, A., Mulumeoderhwa, P.,... & Balaluka, G. (2021). Infant Mortality in Rural and Post-Conflict Areas in South Kivu, Eastern DR Congo: A Cross-Sectional Study.
[24] Tshinu, G. M. (2022). Contribution of Mining Operations Towards Education, Healthcare, Food Security, Housing, Sports, and Recreation in Katanga Province of the DRC. In Handbook of Research on Resource Management and the Struggle for Water Sustainability in Africa (pp. 337-353). IGI Global.
[25] Babawarun, O., Okolo, C. A., Arowoogun, J. O., Adeniyi, A. O., & Chidi, R. (2024). Healthcare managerial challenges in rural and underserved areas: A Review. World Journal of Biology Pharmacy and Health Sciences, 17(2), 323-330.
[26] Mpoy, C. W., Katembo, B. M., Ndomba, M. M., Mishika, P. L., Missumba, W. K., Mukuku, O., & Wembonyama, S. O. (2022). Determinants of utilization and quality of antenatal care services in Lubumbashi, in the Democratic Republic of the Congo. Global Journal of Medical, Pharmaceutical, and Biomedical Update, 17.
[27] Grundy, J., & Biggs, B. A. (2019). The impact of conflict on immunisation coverage in 16 countries. International journal of health policy and management, 8(4), 211.
[28] Duff, R., Patel, F., Dumouza, A., Brown, L., Embeke, N., Fataki, J.,... & Pickett, C. (2023). Facilitators and barriers to supply-side maternal, newborn, and child health service availability in DRC: a systematic review and narrative synthesis. Journal of Global Health Economics and Policy, 3, e2023005.
[29] Mutombo, C. S., Bakari, S. A., Ntabaza, V. N., Nachtergael, A., Lumbu, J. B. S., Duez, P., & Kahumba, J. B. (2022). Perceptions and use of traditional African medicine in Lubumbashi, Haut-Katanga province (DR Congo): A cross-sectional study. PLoS One, 17(10), e0276325.
[30] Renzaho, A. M. (2020). The need for the right socio-economic and cultural fit in the COVID-19 response in sub-Saharan Africa: examining demographic, economic political, health, and socio-cultural differentials in COVID-19 morbidity and mortality. International journal of environmental research and public health, 17(10), 3445.
[31] Lal, A., Ashworth, H. C., Dada, S., Hoemeke, L., & Tambo, E. (2022). Optimizing pandemic preparedness and response through health information systems: lessons learned from Ebola to COVID-19. Disaster medicine and public health preparedness, 16(1), 333-340.
[32] Carsi Kuhangana, T., Kamanda Mbayo, C., Pyana Kitenge, J., Kazadi Ngoy, A., Muta Musambo, T., Musa Obadia, P.,... & Nemery, B. (2020). COVID-19 pandemic: knowledge and attitudes in public markets in the former Katanga Province of the Democratic Republic of Congo. International journal of environmental research and public health, 17(20), 7441.
[33] Bedson, J., Skrip, L. A., Pedi, D., Abramowitz, S., Carter, S., Jalloh, M. F., & Althouse, B. M. (2021). A review and agenda for integrated disease models including social and behavioural factors. Nature human behaviour, 5(7), 834-846.
[34] Kabuya, K. E. The Impact of Telemedicine on Maternal Health and Equity Outcomes in the Democratic Republic of Congo.
[35] World Health Organization. (2020). Operational framework for primary health care: transforming vision into action.
[36] Tyagi, A. K., & Chahal, P. (2020). Artificial intelligence and machine learning algorithms. In Challenges and applications for implementing machine learning in computer vision (pp. 188-219). IGI Global.
[37] Strielkowski, W., Vlasov, A., Selivanov, K., Muraviev, K., & Shakhnov, V. (2023). Prospects and challenges of the machine learning and data-driven methods for the predictive analysis of power systems: A review. Energies, 16(10), 4025.
[38] Ndwabe, H., Basu, A., & Mohammed, J. (2023). Post pandemic analysis on comprehensive utilization of telehealth and telemedicine. Clinical eHealth.
[39] Pramanik, M. I., Lau, R. Y., Azad, M. A. K., Hossain, M. S., Chowdhury, M. K. H., & Karmaker, B. K. (2020). Healthcare informatics and analytics in big data. Expert Systems with Applications, 152, 113388.
[40] Alowais, S. A., Alghamdi, S. S., Alsuhebany, N., Alqahtani, T., Alshaya, A. I., Almohareb, S. N.,... & Albekairy, A. M. (2023). Revolutionizing healthcare: the role of artificial intelligence in clinical practice. BMC medical education, 23(1), 689.
[41] Palma, H. L. (2022). Native Languages of the Democratic Republic of Congo. In Handbook of Language Policy and Education in Countries of the Southern African Development Community (SADC) (pp. 99-116). Brill.
[42] World malaria report 2023 -- spread view:
[43] Alanazi, Abdullah. "Clinicians’ views on using artificial intelligence in healthcare: opportunities, challenges, and beyond." Cureus 15, no. 9 (2023).
[44] Ramakrishnan, R., Rao, S., & He, J. R. (2021). Perinatal health predictors using artificial intelligence: A review. Women's Health, 17, 17455065211046132.
[45] Yaseen, I., & Rather, R. A. (2024). A Theoretical Exploration of Artificial Intelligence’s Impact on Feto-Maternal Health from Conception to Delivery. International Journal of Women's Health, 903-915.
[46] Kelly, S., Kaye, S. A., & Oviedo-Trespalacios, O. (2023). What factors contribute to the acceptance of artificial intelligence? A systematic review. Telematics and Informatics, 77, 101925.
[47] Siala, Haytham, and Yichuan Wang. "SHIFTing artificial intelligence to be responsible in healthcare: A systematic review." Social Science & Medicine 296 (2022): 114782.
[48] Murikah, W., Nthenge, J. K., & Musyoka, F. M. (2024). Bias and Ethics of AI Systems Applied in Auditing-A Systematic Review. Scientific African, e02281.
[49] Joksimovic, S., Ifenthaler, D., Marrone, R., De Laat, M., & Siemens, G. (2023). Opportunities of artificial intelligence for supporting complex problem-solving: Findings from a scoping review. Computers and Education: Artificial Intelligence, 4, 100138.
[50] Togunwa, T. O., Babatunde, A. O., & Abdullah, K. U. R. (2023). Deep hybrid model for maternal health risk classification in pregnancy: synergy of ANN and random forest. Frontiers in Artificial Intelligence, 6, 1213436.
[51] Silva Rocha, E. D., de Morais Melo, F. L., de Mello, M. E. F., Figueiroa, B., Sampaio, V., & Endo, P. T. (2022). On usage of artificial intelligence for predicting mortality during and post-pregnancy: a systematic review of literature. BMC Medical Informatics and Decision Making, 22(1), 334.
[52] Hlongwane, T. M., Botha, T., Nkosi, B. S., & Pattinson, R. C. (2022). Preventing antenatal stillbirths: An innovative approach for primary health care. South African Family Practice, 64(3).
[53] Mfateneza, E., Rutayisire, P. C., Biracyaza, E., Musafiri, S., & Mpabuka, W. G. (2022). Application of machine learning methods for predicting infant mortality in Rwanda: analysis of Rwanda demographic health survey 2014–15 dataset. BMC Pregnancy and Childbirth, 22(1), 388.
[54] Ochieng’, S., Hariharan, N., Abuya, T., Okondo, C., Ndwiga, C., Warren, C. E.,... & Rajasekharan, S. (2024). Exploring the implementation of an SMS-based digital health tool on maternal and infant health in informal settlements. BMC Pregnancy and Childbirth, 24(1), 222.
[55] Batani, J., & Maharaj, M. S. (2023). Emerging technologies’ role in reducing under-five mortality in a low-resource setting: Challenges and perceived opportunities by public health workers in Makonde District, Zimbabwe. Journal of Child Health Care, 13674935231189790.
[56] Mremi, I. R., George, J., Rumisha, S. F., Sindato, C., Kimera, S. I., & Mboera, L. E. (2021). Twenty years of integrated disease surveillance and response in Sub-Saharan Africa: challenges and opportunities for effective management of infectious disease epidemics. One Health Outlook, 3, 1-15.
[57] Zuhair, V., Babar, A., Ali, R., Oduoye, M. O., Noor, Z., Chris, K.,... & Rehman, L. U. (2024). Exploring the impact of artificial intelligence on global health and enhancing healthcare in developing nations. Journal of Primary Care & Community Health, 15, 21501319241245847.
[58] Alhosani, K., & Alhashmi, S. M. (2024). Opportunities, challenges, and benefits of AI innovation in government services: a review. Discover Artificial Intelligence, 4(1), 18.
[59] Michaels-Strasser, S., Thurman, P. W., Kasongo, N. M., Kapenda, D., Ngulefac, J., Lukeni, B.,... & Malele, F. (2021). Increasing nursing student interest in rural healthcare: lessons from a rural rotation program in Democratic Republic of the Congo. Human Resources for Health, 19, 1-13.
[60] Maternalmortality:
[61] Wen, Z., & Huang, H. (2022). The potential for artificial intelligence in healthcare. Journal of Commercial Biotechnology, 27(4).
[62] Valentin, B. C., Philippe, O. N., Henry, M. M., Salvius, B. A., Suzanne, M. K., Kasali, F. M., & Baptiste, L. S. J. (2024). Ethnomedical Knowledge of Plants Used in Nonconventional Medicine for Wound Healing in Lubumbashi, Haut‐Katanga Province, DR Congo. The Scientific World Journal, 2024(1), 4049263.
[63] Van Noordt, C., & Tangi, L. (2023). The dynamics of AI capability and its influence on public value creation of AI within public administration. Government Information Quarterly, 40(4), 101860.
Cite This Article
  • APA Style

    Kabuya, K. E. (2025). Assessment of the Impact of AI on Reducing Maternal and Infant Mortality During Epidemics in Haut-Katanga. American Journal of Clinical and Experimental Medicine, 13(4), 68-78. https://doi.org/10.11648/j.ajcem.20251304.11

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    ACS Style

    Kabuya, K. E. Assessment of the Impact of AI on Reducing Maternal and Infant Mortality During Epidemics in Haut-Katanga. Am. J. Clin. Exp. Med. 2025, 13(4), 68-78. doi: 10.11648/j.ajcem.20251304.11

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    AMA Style

    Kabuya KE. Assessment of the Impact of AI on Reducing Maternal and Infant Mortality During Epidemics in Haut-Katanga. Am J Clin Exp Med. 2025;13(4):68-78. doi: 10.11648/j.ajcem.20251304.11

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  • @article{10.11648/j.ajcem.20251304.11,
      author = {Kalala Elisée Kabuya},
      title = {Assessment of the Impact of AI on Reducing Maternal and Infant Mortality During Epidemics in Haut-Katanga},
      journal = {American Journal of Clinical and Experimental Medicine},
      volume = {13},
      number = {4},
      pages = {68-78},
      doi = {10.11648/j.ajcem.20251304.11},
      url = {https://doi.org/10.11648/j.ajcem.20251304.11},
      eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajcem.20251304.11},
      abstract = {Maternal and infant mortality remain critical public health challenges in Haut-Katanga, particularly during epidemic periods that strain limited healthcare infrastructure. This study evaluates the impact of Artificial Intelligence (AI) on reducing maternal and infant mortality through a retrospective analysis using generated data from 2015 to 2023. During this period, AI adoption increased from 2% to 25%, accompanied by a decline in maternal mortality from 940 to 840 deaths per 100,000 live births, and infant mortality from 85 to 62 deaths per 1,000 live births. Linear regression analysis indicates that a 1% increase in AI adoption is associated with a reduction of approximately 1.2 maternal deaths per 100,000 and 0.15 infant deaths per 1,000, respectively. Pearson correlation analysis reveals a strong negative relationship between AI adoption and both maternal (r ≈ -0.96) and infant mortality (r ≈ -0.96), and a strong positive correlation between maternal and infant mortality (r ≈ +0.98). Additionally, trends in infectious diseases show notable declines in malaria (r = -0.84) and HIV/AIDS (r = -1.00), while measles (r = +0.83), cholera (r = +0.98), and COVID-19 (r = +0.88) increased over time. AI-based interventions, particularly in epidemic prediction and diagnostics, have contributed to measurable health gains. However, implementation remains constrained by infrastructural deficiencies, limited funding, and low digital health capacity. The findings underscore AI's emerging role in improving health outcomes and emphasize the need for strategic investments in infrastructure, workforce training, and supportive policy frameworks to enhance healthcare delivery and epidemic preparedness in resource-limited settings.},
     year = {2025}
    }
    

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  • TY  - JOUR
    T1  - Assessment of the Impact of AI on Reducing Maternal and Infant Mortality During Epidemics in Haut-Katanga
    AU  - Kalala Elisée Kabuya
    Y1  - 2025/07/04
    PY  - 2025
    N1  - https://doi.org/10.11648/j.ajcem.20251304.11
    DO  - 10.11648/j.ajcem.20251304.11
    T2  - American Journal of Clinical and Experimental Medicine
    JF  - American Journal of Clinical and Experimental Medicine
    JO  - American Journal of Clinical and Experimental Medicine
    SP  - 68
    EP  - 78
    PB  - Science Publishing Group
    SN  - 2330-8133
    UR  - https://doi.org/10.11648/j.ajcem.20251304.11
    AB  - Maternal and infant mortality remain critical public health challenges in Haut-Katanga, particularly during epidemic periods that strain limited healthcare infrastructure. This study evaluates the impact of Artificial Intelligence (AI) on reducing maternal and infant mortality through a retrospective analysis using generated data from 2015 to 2023. During this period, AI adoption increased from 2% to 25%, accompanied by a decline in maternal mortality from 940 to 840 deaths per 100,000 live births, and infant mortality from 85 to 62 deaths per 1,000 live births. Linear regression analysis indicates that a 1% increase in AI adoption is associated with a reduction of approximately 1.2 maternal deaths per 100,000 and 0.15 infant deaths per 1,000, respectively. Pearson correlation analysis reveals a strong negative relationship between AI adoption and both maternal (r ≈ -0.96) and infant mortality (r ≈ -0.96), and a strong positive correlation between maternal and infant mortality (r ≈ +0.98). Additionally, trends in infectious diseases show notable declines in malaria (r = -0.84) and HIV/AIDS (r = -1.00), while measles (r = +0.83), cholera (r = +0.98), and COVID-19 (r = +0.88) increased over time. AI-based interventions, particularly in epidemic prediction and diagnostics, have contributed to measurable health gains. However, implementation remains constrained by infrastructural deficiencies, limited funding, and low digital health capacity. The findings underscore AI's emerging role in improving health outcomes and emphasize the need for strategic investments in infrastructure, workforce training, and supportive policy frameworks to enhance healthcare delivery and epidemic preparedness in resource-limited settings.
    VL  - 13
    IS  - 4
    ER  - 

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Author Information
  • Department of Public Health, Africa University, Mutare, Zimbabwe

  • Abstract
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    1. 1. Introduction
    2. 2. Literature Review
    3. 3. Methodology
    4. 4. Results
    5. 5. Discussion
    6. 6. Conclusion
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