Design of an AI-Powered Platform for Personalized Medicine Using Genomic Data: A Case Study of University of Abuja, FCT
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- NGN 5000
Background of the Study :
The rapid convergence of artificial intelligence (AI) and genomics has ushered in a new era in personalized medicine, where treatments can be tailored to an individual’s genetic profile. This study focuses on the design of an AI-powered platform that leverages genomic data to provide personalized therapeutic recommendations. With the advent of high-throughput sequencing and big data analytics, vast amounts of genomic information are now available, yet their effective interpretation remains a significant challenge (Adebola, 2023). An AI-driven approach can integrate complex genetic datasets with clinical records, thereby enabling precise identification of disease biomarkers and risk factors. Such a platform can enhance diagnostic accuracy, optimize drug selection, and reduce adverse reactions by tailoring interventions to the genetic makeup of patients (Ibrahim, 2024). Moreover, the use of machine learning algorithms facilitates the recognition of hidden patterns within the data, making it possible to predict patient-specific responses to various treatments. The University of Abuja, being a hub of academic and clinical research, provides an ideal setting for the development and testing of such innovative technologies. The study underscores the importance of data security and ethical considerations in handling sensitive genomic information while ensuring compliance with international standards. Additionally, the integration of real-time data analysis and feedback mechanisms into the platform is expected to foster continuous improvement of the personalized medicine approach. By combining computational power with biological insights, the platform seeks to transform traditional medical practices into a more dynamic, patient-centered model (Chukwu, 2025). The research also addresses the scalability of the system, ensuring that it can accommodate future advances in genomic technologies and expanding databases. In summary, the proposed AI-powered platform represents a promising convergence of bioinformatics and clinical care, with the potential to revolutionize healthcare delivery by offering customized, efficient, and effective treatment strategies.
Statement of the Problem :
Despite the promising advances in AI and genomics, the implementation of personalized medicine remains hampered by several challenges. First, the integration of heterogeneous genomic datasets with clinical data poses significant technical and computational hurdles. Existing systems often lack the robustness required to process and analyze high-dimensional data in real-time, leading to delays in diagnosis and suboptimal treatment recommendations (Okoro, 2023). Moreover, the complexity of genetic variations and their interactions with environmental factors makes it difficult to develop algorithms that can accurately predict patient responses. There is also the critical issue of data privacy, as the management of sensitive genetic information necessitates stringent security measures and ethical oversight (Eze, 2024). Additionally, many healthcare providers in resource-limited settings are not fully equipped with the digital infrastructure or expertise required to adopt such advanced platforms. The lack of interoperability between various healthcare information systems further complicates the seamless integration of genomic data into clinical workflows. This study aims to address these challenges by designing a platform that not only incorporates state-of-the-art AI algorithms but also adheres to rigorous standards of data security and usability. The research will explore innovative methods to streamline data integration and improve the interpretability of complex genomic information. By validating the platform within the University of Abuja’s clinical environment, the study intends to demonstrate its potential to enhance diagnostic accuracy and therapeutic outcomes. Ultimately, the project seeks to bridge the gap between cutting-edge genomic research and practical, patient-centered healthcare, thereby laying the groundwork for a more personalized, efficient, and secure medical practice (Ibrahim, 2025).
Objectives of the Study:
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To design an AI-powered platform that integrates genomic and clinical data for personalized medicine.
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To evaluate the platform’s performance in accurately predicting patient-specific treatment outcomes.
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To assess the usability and security features of the platform in a real-world clinical setting.
Research Questions:
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How can AI algorithms be optimized to analyze genomic data for personalized treatment recommendations?
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What are the major technical and ethical challenges in integrating genomic data into clinical workflows?
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How does the platform improve diagnostic accuracy and treatment outcomes compared to traditional methods?
Significance of the Study:
This study is significant as it pioneers the integration of AI with genomic data to enable personalized medicine. The platform promises to enhance diagnostic precision, reduce treatment-related adverse events, and optimize healthcare delivery by tailoring interventions to individual genetic profiles. The outcomes will inform future policy, improve clinical decision-making, and drive innovations in bioinformatics and personalized healthcare (Balogun, 2024).
Scope and Limitations of the Study:
The study is limited to the design, development, and evaluation of an AI-powered personalized medicine platform using genomic data from the University of Abuja, FCT. It does not extend to nationwide implementation or long-term clinical outcome studies.
Definitions of Terms:
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AI-Powered Platform: A software system that employs artificial intelligence algorithms to process and analyze data.
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Personalized Medicine: A medical approach that tailors treatment to the individual characteristics of each patient, often based on genetic information.
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Genomic Data: Information derived from an individual’s complete DNA sequence, including gene variations and mutations.
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