AI-DRIVEN PRECISION ONCOLOGY: REVOLUTIONIZING PERSONALIZED CANCER THERAPY

Precision oncology is an approach in which cancer treatment is selected according to the molecular, genomic and clinical characteristics of an individual patient and tumour. Artificial intelligence (AI), including machine learning, deep learning and natural language processing, can analyses large and complex datasets generated by genomics, pathology, radiology and electronic health records. AI may help identify actionable biomarkers, predict treatment response and resistance, support drug discovery, match patients to clinical trials and anticipate adverse drug reactions (ADRs). This review describes the principles of AI-driven precision oncology, major biomarkers, pharmacogenomics, mechanisms of selected targeted and immune therapies, drug-specific adverse effects, AI-assisted toxicity prediction, clinical workflow, advantages, limitations, ethical considerations and future directions. Selected therapies including osimertinib, trastuzumab, dabrafenib/trametinib, olaparib and pembrolizumab demonstrate how molecular findings can be translated into individualized treatment. The future of oncology is likely to involve multimodal AI combined with longitudinal molecular monitoring and multidisciplinary clinical decision-making.