Explainable artificial intelligence in prostate and bladder cancer: A review of imaging, digital pathology, molecular profiling, and clinical data
TL;DR
This narrative review synthesizes evidence across both cancers in imaging, digital pathology, molecular and liquid-biopsy data, and clinical applications and assesses deployment readiness and explains explainability without post hoc approximation.
Abstract
Artificial intelligence (AI) models are increasingly applied across the detection, staging, treatment selection, and surveillance of prostate and bladder cancer. Explainable AI links predictions to identifiable features, spatial regions, or architectural constraints, supporting clinical audit and informed use. This narrative review synthesizes evidence across both cancers in imaging, digital pathology, molecular and liquid-biopsy data, and clinical applications and assesses deployment readiness. Two parallel structured searches of PubMed/MEDLINE were performed, one for each cancer, covering records through November 15, 2025. Eligible studies applied machine or deep learning to a clinical or translational task and included an explainability component. Seventy studies across 4 data domains were included. Imaging was the most active domain. SHapley Additive exPlanations and gradient-weighted class activation mapping confirmed biologically plausible signal localization. Interpretability-by-design in bladder endoscopy embedded explainability without post hoc approximation. Attention heatmaps and probability maps in digital pathology linked model focus to histological features relevant to grading and invasion. Molecular models ranged from post hoc attribution to pathway-constrained architectures; a biologically structured deep neural network in prostate cancer demonstrated how architectural constraints can move explanations toward mechanistic inference. Clinical and electronic medical record-based models constituted the largest group, using SHapley Additive exPlanations for prebiopsy triage, treatment selection, recurrence prediction, and active surveillance. Post hoc explanations and interpretable-by-design models carry different evidentiary weight. Prospective evidence that explainable AI affects clinical decisions is lacking. Translation requires independent validation of fidelity, stability, and usability, prospective reader studies, standardized reporting, and investment in interpretable architectures.