Development of an AI‐Based System for Personalized Medicine Using Genomic Data: A Case Study of Federal University, Dutse, Jigawa State
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Background of the Study
Personalized medicine represents a paradigm shift in healthcare, emphasizing tailored treatment strategies based on an individual’s genetic profile. With the rapid advancement in genomic sequencing technologies, vast amounts of genomic data are now available, offering unprecedented opportunities to customize medical interventions. At Federal University, Dutse, Jigawa State, the development of an AI‐based system for personalized medicine is being pursued to harness the potential of genomic data in clinical decision‐making. Artificial intelligence, particularly machine learning and deep learning algorithms, provides powerful tools to analyze complex genomic datasets and predict patient‐specific responses to various therapies (Amin, 2023). The integration of AI in personalized medicine involves the aggregation of genomic, proteomic, and clinical data to identify biomarkers that can guide treatment selection and dosing. This approach not only enhances treatment efficacy but also minimizes adverse drug reactions by considering the unique genetic makeup of each patient. The proposed system is designed to automate the analysis process, providing clinicians with real‐time insights and predictive models that can inform therapeutic strategies. By employing iterative learning algorithms, the system continuously refines its predictive accuracy as more data becomes available, ensuring that the recommendations remain up‐to‐date with the latest clinical evidence (Chukwu, 2024). Additionally, the project emphasizes user‐friendly interfaces and seamless integration with existing hospital information systems, making it accessible to clinicians without extensive computational expertise. The interdisciplinary collaboration between data scientists, geneticists, and medical professionals at Federal University, Dutse, underscores the commitment to advancing personalized healthcare. This initiative is expected to not only improve patient outcomes by facilitating more precise treatment regimens but also contribute to the broader field of computational genomics by providing a model for AI integration in clinical settings (Fasola, 2025).
Statement of the Problem
Despite the promising potential of personalized medicine, the integration of genomic data into routine clinical practice remains a significant challenge. At Federal University, Dutse, Jigawa State, current diagnostic and treatment protocols often rely on generalized approaches that do not account for individual genetic variability. The lack of an efficient, AI‐based system for analyzing genomic data results in missed opportunities for optimizing therapeutic strategies. Traditional methods are frequently hindered by the sheer volume and complexity of genomic information, leading to delays in data processing and inaccuracies in patient‐specific recommendations (Ojo, 2023). Furthermore, existing systems are not adequately equipped to integrate diverse data sources, such as genomic, proteomic, and clinical records, which are essential for comprehensive personalized medicine. This fragmentation results in inconsistent treatment outcomes and an increased risk of adverse drug reactions. The absence of real‐time data analysis and predictive capabilities further exacerbates the problem, as clinicians are unable to access timely insights that could inform critical treatment decisions. This study aims to address these issues by developing an AI‐based system that automates the integration and analysis of genomic data, thereby providing accurate and personalized treatment recommendations. By incorporating advanced machine learning algorithms, the system is designed to overcome the limitations of current methodologies, ensuring rapid processing and high predictive accuracy. Addressing these challenges is vital for enhancing the effectiveness of personalized medicine, reducing healthcare costs, and improving patient outcomes. The research will critically evaluate existing diagnostic workflows and propose a novel framework that bridges the gap between genomic data analysis and clinical application (Mustapha, 2024).
Objectives of the Study
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To develop an AI‐based system for analyzing genomic data to support personalized medicine.
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To integrate genomic, proteomic, and clinical data for comprehensive patient profiling.
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To evaluate the system’s effectiveness in providing accurate, personalized treatment recommendations.
Research Questions
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How can AI improve the integration and analysis of genomic data for personalized medicine?
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What are the key challenges in merging diverse biomedical datasets?
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How effective is the AI‐based system in predicting patient‐specific treatment outcomes?
Significance of the Study
This study is significant as it pioneers the development of an AI‐based system for personalized medicine, harnessing genomic data to tailor treatment strategies. By integrating multiple data sources and employing advanced machine learning algorithms, the system aims to enhance diagnostic precision and optimize therapeutic interventions. The research will contribute to reducing adverse drug reactions and improving patient outcomes, setting a precedent for personalized healthcare in resource‐limited settings (Fasola, 2025).
Scope and Limitations of the Study
The study is limited to the development and evaluation of an AI‐based system for personalized medicine using genomic data at Federal University, Dutse, Jigawa State. It focuses solely on integrating genomic, proteomic, and clinical data without extending to other forms of biomedical data.
Definitions of Terms
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Personalized Medicine: A medical approach that tailors treatment based on individual genetic and clinical information.
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Genomic Data: The complete set of DNA sequences and genetic information of an individual.
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Machine Learning: A subset of AI involving algorithms that improve automatically through experience and data analysis.
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