Artificial Intelligence in Digital Pathology for Prostate Cancer Detection: FDA Clearance and Real-World Implementation

Artificial intelligence in prostate cancer diagnosis and management: state-of-the-art approaches and emerging innovations.

Explainable artificial intelligence in prostate and bladder cancer: A review of imaging, digital pathology, molecular profiling, and clinical data

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.

Autonomous AI in prostate cancer: the road ahead towards clinical implementation.

The findings highlight the need for evidence from large, multicenter, prospective trials and evaluation frameworks that reflect the consequences of clinical decision-making, as well as further exploration of safeguards to monitor and address mismatches between training data and incoming scans during deployment.

Artificial intelligence in digital pathology diagnosis and analysis: Technologies, clinical integration, and future prospects (Review)

The present review analyzes the existing context of AI pathology systems, particularly diagnostic precision, clinical validation, and technical systems such as convolutional neural networks and transformers and discusses the integration challenge in clinical workflows for these systems.

Artificial intelligence in prostate biopsy: diagnostic applications, risk stratification, and precision oncology

Prostate biopsy remains the cornerstone for the diagnosis, risk stratification, and management of prostate cancer. However, biopsy interpretation is challenged by sampling limitations, tumor heterogeneity, and interobserver variability in Gleason grading. Recent advances in digital pathology and artificial intelligence (AI) have created new opportunities to improve diagnostic accuracy, reproducibility, and clinical decision-making. This narrative mini-review summarizes current evidence regarding AI applications in prostate biopsy evaluation. Relevant studies published between 2015 and 2026 were reviewed, focusing on AI-assisted cancer detection, automated Gleason grading, quantitative pathology, prognostic assessment, and multimodal approaches integrating histopathological, molecular, and clinical data. AI-based systems have demonstrated high accuracy in prostate cancer detection, Gleason pattern classification, and tumor burden assessment, with several studies showing strong concordance with expert genitourinary pathologists. These technologies have the potential to improve diagnostic consistency, enhance workflow efficiency, refine risk stratification, and support treatment planning. Emerging multimodal AI models integrating histopathological, genomic, imaging, and clinical information may further improve prognostic assessment and facilitate precision oncology approaches. However, challenges remain, including limited prospective validation, data heterogeneity, regulatory considerations, model interpretability, and integration into routine clinical workflows. AI is emerging as a valuable adjunct in prostate biopsy evaluation, with the potential to enhance diagnostic precision, grading reproducibility, and personalized patient management. Continued multicenter validation, development of explainable AI frameworks, and effective integration into multidisciplinary prostate cancer care pathways will be essential for successful clinical adoption and improved patient outcomes.

The Use of Artificial Intelligence in Cancer Diagnosis.

Artificial intelligence (AI) is increasingly influencing cancer diagnostics, with ongoing advancements into more refined and advanced applications. This review examines current applications of AI in cancer diagnosis. AI has demonstrated considerable success in screening and diagnosis of a range of cancer types, including breast, colorectal, and skin cancer, but challenges with specificity and false-positive rates remain. Within radiology and histopathology, AI shows promise in identifying cancers and augmenting clinician performance. Natural language processing and AI-driven clinical documentation tools can expedite diagnosis by reducing administrative burden and extracting clinically meaningful data from unstructured records. Additionally, AI models using unimodal and multimodal data can improve patient prognostication and risk stratification. Despite these advances, challenges relating to deployment, bias, interpretability, accountability and real-world efficacy persist. Prospective validation and careful implementation are required to ensure safe, equitable, and effective adoption of AI in cancer diagnostics.