Artificial Intelligence-Driven Oncology: Current Implementations, Computational Complexities, and Emerging Research Frontiers

Abstract

Artificial Intelligence (AI) has emerged as a transformative tool in oncological pharmacology, significantly enhancing drug discovery, treatment personalization, and patient care. This paper explores the evolving role of AI in cancer pharmacotherapy, focusing on its applications in drug response prediction, dose optimization, immunotherapy outcome forecasting, and clinical trial design. Through the integration of machine learning (ML), deep learning (DL), and generative models, AI is enabling personalized oncology by analyzing high-dimensional data, including genomics, proteomics, and clinical records. Key AI applications, such as virtual screening of drug libraries, pharmacokinetic-pharmacodynamic modeling, and reinforcement learning for dynamic treatment optimization, are reshaping cancer care. However, challenges remain, including data heterogeneity, algorithmic bias, and the need for regulatory frameworks for AI tools. Emerging approaches like federated learning, explainable AI (XAI), and causal inference hold promise for addressing these limitations. The paper concludes by discussing the potential of AI to bridge the gap between complex molecular data and individualized treatment plans, emphasizing the need for ethical deployment, regulatory compliance, and clinician trust to ensure equitable and sustainable advancements in cancer treatment.