Explainable AI in Oncology: Methods to Open the Black Box
This review surveys explainable AI methodologies applied to oncology, covering LIME, SHAP, Grad-CAM, Integrated Gradients, and newer approaches like ProtoPNet and counterfactual explanations. While these tools improve interpretability of AI predictions from imaging, genomics, and pathology data, challenges remain around computational cost, explanation consistency, and regulatory compliance. The paper provides a practical overview for labs considering AI adoption.
The original study
Overcoming the Black Box Challenge: Building Trust in Artificial Intelligence Algorithms in Oncology.
- Authors
- Alum EU, Egwu CK, Manjula VS, Ekpang PO, Ekpang JE, Echegu DA, et al.
- Journal
- Technology in cancer research & treatment
- PMID
- 41873493
Original abstract
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 diverse oncology 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 most AI systems is still limited by the black box issue, that is, prediction without clear explanation, which, in turn, limits the confidence and accountability of clinicians as well as their ability to communicate with patients. In this review, we searched sources over the years (2015-2025) from PubMed, Scopus, and Web of Science for evidence on explainable AI (XAI) methodologies that may provide greater 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 understand the main influential factors behind model predictions. The complementary approaches, such as Gradient-weighted Class Activation Mapping (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, privacy issues, 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, and patient-centered communication. By providing a comprehensive and clinically grounded overview, this review aims to support researchers, clinicians, and stakeholders in advancing trustworthy and transparent AI deployment in oncology.