AI Driven Precision Oncology: Predictive Biomarker Discovery and Personalized Treatment Optimization Using Genomic Data

Precision oncology has revolutionized cancer treatment by leveraging genomic data to develop targeted therapies tailored to individual patients. Artificial intelligence (AI), particularly machine learning and deep learning models, has emerged as a powerful tool for predictive biomarker discovery and personalized treatment optimization. This paper explores the role of AI-driven methodologies in identifying predictive biomarkers, stratifying patients based on molecular profiles, and optimizing treatment regimens to improve therapeutic efficacy while minimizing adverse effects. Through high-dimensional genomic analysis, AI algorithms can uncover hidden patterns in multi-omics datasets, including gene expression, mutations, and epigenetic modifications, to predict treatment responses and disease progression. Reinforcement learning and neural network-based approaches enable adaptive treatment strategies, allowing for real-time therapeutic adjustments based on evolving patient-specific genomic landscapes. Additionally, AI-driven computational frameworks integrate clinical and genomic data to enhance precision medicine initiatives, reducing trial-and-error treatment approaches and improving patient outcomes. This study examines the latest advancements in AI-driven precision oncology, highlighting successful applications in biomarker discovery, drug repurposing, and response prediction for immunotherapy and chemotherapy. Furthermore, we address key challenges, including data privacy concerns, model interpretability, and the need for robust validation in clinical settings. A comparative analysis of AI-based personalized treatment models versus traditional clinical decision-making underscores the transformative potential of AI in oncology. Future directions emphasize refining AI models for real-time clinical application, integrating multi-omics data for enhanced predictive accuracy, and addressing ethical considerations in AI-driven cancer treatment.