Explainable Artificial Intelligence in Oncology: Building Transparent and Trustworthy Clinical Decision Support Systems

Abstract

Cancer remains one of the leading causes of morbidity and mortality worldwide despite remarkable advances in molecular biology, precision medicine, targeted therapeutics, and immunotherapy. Artificial intelligence (AI) has emerged as a transformative technology in oncology by enabling automated analysis of radiological imaging, digital pathology, genomic sequencing, multi-omics, electronic health records, laboratory investigations, and longitudinal clinical data. Although machine learning and deep learning algorithms have demonstrated outstanding performance in cancer diagnosis, prognostic prediction, molecular characterization, treatment optimization, and clinical decision support, many high-performing AI models operate as "black boxes," limiting clinician confidence and hindering routine clinical adoption. Explainable artificial intelligence (XAI) has therefore emerged as a critical discipline focused on making AI systems transparent, interpretable, trustworthy, and clinically accountable. Through techniques including saliency maps, attention visualization, SHAP (Shapley Additive Explanations), Local Interpretable Model-Agnostic Explanations (LIME), counterfactual reasoning, causal inference, concept attribution, and uncertainty estimation, XAI enables clinicians to understand how AI models generate predictions while supporting evidence-based decision-making. Explainable AI is increasingly being integrated into radiology, computational pathology, genomics, precision therapeutics, digital twins, multimodal foundation models, and autonomous clinical decisionsupport systems. Despite substantial advances, challenges remain regarding explanation fidelity, computational complexity, standardization, regulatory validation, ethical governance, and clinician acceptance. This review provides a comprehensive overview of explainable artificial intelligence in oncology, emphasizing computational foundations, current clinical applications, implementation challenges, and future perspectives for building transparent and trustworthy clinical decisionsupport systems.