PREDICTION OF CHEMOTHERAPY EFFECTIVENESS AND ACCURACY USING ARTIFICIAL INTELLIGENCE
Abstract
The effectiveness of chemotherapy varies significantly among cancer patients due to genetic, molecular, and environmental factors, making personalized treatment strategies critical for improving outcomes. Traditional methods for predicting chemotherapy response rely on clinical staging, histopathology, and clinician experience, which often fail to account for complex patient-specific variability. Artificial Intelligence (AI) and machine learning (ML) offer powerful tools to analyze multidimensional biomedical data, including genomic profiles, imaging, and clinical records, to predict chemotherapy responsiveness. This thesis examines the role of AI in predicting chemotherapy effectiveness, highlighting algorithmic approaches, data integration strategies, clinical applications, and challenges. By leveraging AI, clinicians can enhance precision oncology, optimize treatment plans, and improve patient survival while minimizing adverse effects.
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Copyright (c) 2025 Fazliddin Arziqulov, Sayfullayeva Dilbar Izzatillayevna, Maxsudov Valijon Gafurjonovich (Author)
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