AI-Driven Personalized Cancer Treatment: Integrating Genomic Data for Targeted Therapy Decisions Review Article

Abstract

Artificial Intelligence (AI) has emerged as a transformative force in oncology, enabling the integration of genomic and multi-omic data for truly personalized cancer care. Conventional cancer management often relied on generalized treatment protocols that overlooked genetic and phenotypic diversity among patients. Advances in machine learning (ML) and deep learning (DL) now allow AI systems to analyze high-dimensional datasets including genomics, transcriptomics, proteomics, imaging, and clinical variables thus guiding tailored therapies with improved precision and reduced toxicity.

Next-Generation Sequencing (NGS) provides detailed tumor mutation profiles, while AI-driven feature selection and data fusion enhance biomarker discovery, risk stratification, and therapy adaptation. Applications span the cancer care continuum: AI-powered diagnostics improve tumor detection, predictive models anticipate resistance, and Clinical Decision Support Systems (CDSS) assist oncologists with real-time, evidence-based decisions. Additionally, AI accelerates drug discovery, virtual screening, and nanomedicine design, offering efficient and targeted treatment options. Case studies in lung, breast, and rare cancers demonstrate significant improvements in diagnosis, therapy selection, and survival outcomes.

Nonetheless, challenges persist, including data privacy, algorithm transparency, infrastructure needs, and regulatory gaps. Ethical issues such as algorithmic bias and equitable access require urgent attention. Future directions emphasize reinforcement learning, causal inference, and multimodal integration to refine adaptive therapies. Ultimately, AI-driven genomic oncology marks a paradigm shift toward predictive, precise, and equitable cancer treatment, improving both survival and quality of life.