Integration of Artificial Intelligence in Medical Oncology: Prediction of Treatment Response, Toxicity, and Survival

Artificial intelligence is increasingly transforming medical oncology by enabling the integration and analysis of large-scale and heterogeneous data derived from clinical records, medical imaging, genomics, transcriptomics, and digital pathology. Advances in machine learning and deep learning have led to the development of predictive models capable of anticipating treatment response, therapy-related toxicities, and patient survival with improved accuracy. By capturing complex and non-linear interactions inherent to cancer biology, multimodal artificial intelligence approaches consistently outperform conventional prognostic tools and single biomarkers. This review provides a concise and comprehensive synthesis of current applications of artificial intelligence in medical oncology, with a particular focus on response prediction, toxicity risk assessment, and survival modeling. It further examines the clinical implications of these technologies, addresses methodological, regulatory, ethical, and economic challenges, and emphasizes the importance of explainability, external validation, and prospective evaluation. Finally, the review outlines future perspectives for the safe and effective integration of artificial intelligence into routine oncology practice, positioning it as a key component of precision oncology rather than a replacement for clinical expertise.