Abstract
Harnessing artificial intelligence (AI) for HIV drug resistance prediction and personalized treatment represented a transformative approach in managing HIV/AIDS. This review explored the integration of AI methodologies, particularly machine learning and deep learning, to enhance the prediction of drug resistance mutations in HIV. By analyzing genomic sequences and clinical data, AI models can identify patterns associated with resistance, enabling clinicians to tailor antiretroviral therapy (ART) to individual patient profiles. The review discussed various AI techniques, including random forests, support vector machines, and neural networks, highlighting their effectiveness in predicting resistance and improving treatment outcomes. The methodology employed in this review involved a comprehensive analysis of recent literature and case studies to evaluate the performance and applicability of AI-driven predictive models in clinical settings. Keywords: Artificial Intelligence, HIV Drug Resistance, Personalized Treatment, Machine Learning, Genomic Data.
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