AI-BASED SELECTION OF PERSONALIZED CANCER THERAPY
Abstract
Cancer therapy has evolved from standard, one-size-fits-all regimens to personalized approaches tailored to individual patient profiles. Despite advances in genomics and precision medicine, selecting the optimal therapy remains challenging due to tumor heterogeneity, complex molecular interactions, and patient-specific factors. Artificial Intelligence (AI) provides a transformative solution by integrating multi-dimensional biomedical data—including genomics, transcriptomics, proteomics, imaging, and clinical history—to guide personalized treatment selection. This thesis examines AI-based systems for cancer therapy selection, highlighting computational methodologies, clinical applications, advantages, challenges, and future directions. AI-driven approaches have demonstrated improved accuracy in therapy prediction, enhanced patient outcomes, and reduced treatment-related toxicity, representing a paradigm shift in precision oncology.
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Copyright (c) 2025 Fazliddin Arziqulov, Sayfullayeva Dilbar Izzatillayevna, Maxsudov Valijon Gafurjonovich (Author)
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