THE IMPACT OF ARTIFICIAL INTELLIGENCE-DRIVEN EARLY DETECTION OF NON-COMMUNICABLE DISEASES: A CASE STUDY OF UNIVERSITY OF MAIDUGURI TEACHING HOSPITAL, BORNO STATE
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Background of the Study
Non-communicable diseases (NCDs), such as diabetes, hypertension, and cancer, are major contributors to global mortality and morbidity. The World Health Organization (WHO) reports that NCDs account for approximately 74% of deaths worldwide, with a disproportionate impact in low- and middle-income countries. In Nigeria, the burden of NCDs is rising due to changing lifestyles, aging populations, and limited preventive healthcare systems. Early detection is critical to reducing the impact of NCDs, as it allows for timely intervention and better management, improving patient outcomes and reducing healthcare costs.
Artificial Intelligence (AI) offers a powerful tool for transforming the detection and management of NCDs. AI-driven diagnostic systems can analyze vast amounts of medical data, identify patterns, and predict disease risks with high accuracy. For example, machine learning algorithms can process imaging data to detect early-stage cancers or analyze electronic health records to flag individuals at risk of hypertension or diabetes. These applications enhance the capacity of healthcare facilities, particularly in resource-constrained settings, to provide timely and accurate diagnoses.
University of Maiduguri Teaching Hospital (UMTH), a leading healthcare facility in northeastern Nigeria, faces significant challenges in managing the rising burden of NCDs. While the hospital serves a large population, its resources are often overstretched, and delays in diagnosis are common. Integrating AI technology into the hospital's diagnostic processes could alleviate some of these challenges, enabling earlier detection and better patient outcomes.
This study examines the potential of AI-driven technologies for early detection of NCDs in UMTH. By focusing on this critical issue, the research aims to provide actionable insights into how AI can be harnessed to strengthen healthcare delivery in Nigeria and contribute to achieving global health targets such as Universal Health Coverage (UHC).
Statement of the Problem
Non-communicable diseases are becoming increasingly prevalent in Nigeria, placing a heavy burden on healthcare facilities such as UMTH. However, the lack of effective early detection mechanisms exacerbates the impact of these diseases, leading to late presentations and poor health outcomes. AI technology offers a promising solution to this problem, but its adoption in clinical practice remains limited in Nigeria. This study investigates how AI-driven early detection tools can enhance the diagnostic capacity of UMTH, improving outcomes for patients with NCDs.
Aim and Objectives of the Study
Aim:
To explore the impact of Artificial Intelligence-driven early detection systems on the management of non-communicable diseases at the University of Maiduguri Teaching Hospital.
Objectives:
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To assess the current diagnostic challenges related to NCDs at UMTH.
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To examine the application of AI-driven tools in the early detection of NCDs.
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To evaluate the impact of AI technology on patient outcomes and healthcare efficiency at UMTH.
Research Questions
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What are the diagnostic challenges faced by UMTH in managing non-communicable diseases?
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How can AI-driven systems improve the early detection of NCDs at UMTH?
Research Hypotheses
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AI-driven early detection systems significantly improve diagnostic accuracy for NCDs.
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The integration of AI technology reduces diagnostic delays at UMTH.
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AI applications enhance overall patient outcomes in the management of NCDs.
Significance of the Study
This research highlights the potential of AI-driven early detection systems to address the growing burden of NCDs in Nigeria. By focusing on UMTH, the study offers practical recommendations for integrating AI into clinical workflows, contributing to improved healthcare delivery and reduced mortality rates associated with NCDs.
Scope and Limitation of the Study
The study is limited to the use of AI-driven early detection tools in diagnosing NCDs at UMTH. While the findings may provide valuable insights, their applicability to other healthcare facilities in Nigeria may be influenced by differences in resources, infrastructure, and patient populations.
Definition of Terms
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Artificial Intelligence (AI): Advanced computer systems designed to mimic human intelligence, including learning, reasoning, and decision-making.
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Non-Communicable Diseases (NCDs): Chronic diseases not spread from person to person, such as diabetes, cardiovascular diseases, and cancer.
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Early Detection: The identification of diseases at an early stage, enabling timely intervention and better management.
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