AI and Predictive Models for Adverse Drug Reactions in Oncology: Recent Advances and Clinical Applications

Adverse drug reactions (ADRs) represent a significant challenge in oncology, contributing to treatment discontinuation, increased healthcare costs, and compromised patient outcomes. The integration of artificial intelligence (AI) and machine learning (ML) has revolutionized the prediction and management of chemotherapy-induced toxicities. This review evaluates recent advances in AI-driven predictive models for ADR detection in oncology, focusing on supervised and deep learning algorithms, natural language processing techniques, and personalized treatment optimization platforms. Machine learning models, particularly ensemble methods such as XGBoost, Random Forest, and gradient boosting algorithms, have demonstrated high predictive accuracy (AUC 0.80-0.97) across various toxicity endpoints including cardiotoxicity, nephrotoxicity, hepatotoxicity, and hematologic complications. Deep learning approaches utilizing convolutional and recurrent neural networks show promise in processing multi-modal data from electronic health records, imaging, and genomic profiles. Natural language processing enables extraction of ADR information from unstructured clinical notes, enhancing real-time pharmacovigilance. Despite these advances, challenges including data heterogeneity, model interpretability, clinical integration, and regulatory compliance remain. This review synthesizes current evidence on AI methodologies for ADR prediction, discusses clinical implementation strategies, and identifies future directions toward precision oncology and personalized chemotherapy dosing.