Overcoming the Black Box Challenge: Building Trust in Artificial Intelligence Algorithms in Oncology
Rising global cancer rates are projected to significantly increase by 2050, highlighting the urgent need for improved scalable prevention, early detection, and personalized therapy tools. Artificial intelligence (AI) has demonstrated significant capabilities in diverseoncology tasks, leveraging high-dimensional data from medical imaging, molecular profiles, and electronic health records for applications in radiology, digital pathology, genomics, prognostication, and treatment selection. Nevertheless, the clinical adoption of mostAI systems is still limited by the black box issue, that is, prediction without clear explanation, which, in turn, limits the confidenceand accountability of clinicians as well as their ability to communicate with patients. In this review, we searched sources over theyears (2015-2025) from PubMed, Scopus, and Web of Science for evidence on explainable AI (XAI) methodologies that may providegreater interpretability and trust in oncologic practice. Local interpretable model-agnostic explanation and Shapley additive explanations (LIME and SHAP) are model-agnostic methods that offer local and global feature attribution and help clinicians to understandthe main influential factors behind model predictions. The complementary approaches, such as Gradient-weighted Class ActivationMapping (Grad-CAM), Integrated Gradients and DeepLift, also bring the explainability to image- and genomics-based processes,whereas more recent strategies (eg, Anchors, Prototypical Part Network (ProtoPNet), and contrastive or counterfactual explanations) also focus on enhancing stability and clinical utility. Irrespective of such developments, several issues continue to be experienced, including computational load, inconsistency in explanations, domain transfer, deployment into clinical processes, bias, privacyissues, and changing regulatory requirements. In general, XAI can transform oncology AI to become clinically interpretable, transparent prediction of outcomes, which will make its application safer by adhering to strict validation procedures, human control, andpatient-centered communication. By providing a comprehensive and clinically grounded overview, this review aims to supportresearchers, clinicians, and stakeholders in advancing trustworthy and transparent AI deployment in oncology.