ReviewCurrent oncology (Toronto, Ont.)2026
Current Applications and Future Directions of Artificial Intelligence in Prostate Cancer Diagnosis: A Narrative Review.
Review in Current oncology (Toronto, Ont.), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
2 citing papers in PubMed.
- Olfactory Science and Technology in Prostate Cancer Diagnosis: From Invertebrate Models to Artificial Intelligence.Life (Basel, Switzerland) · 2026Review
- Artificial intelligence in prostate biopsy: diagnostic applications, risk stratification, and precision oncology.Frontiers in oncology · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Prostate cancer (PCa) remains a major global health challenge, yet conventional diagnostic methods are often limited by suboptimal accuracy and efficiency. Artificial intelligence (AI) has emerged as a rapidly developing technology capable of integrating multi-source data to enhance clinical decision-making. This narrative review synthesizes current evidence regarding AI applications across key diagnostic domains, including medical imaging, digital pathology, liquid biopsy, and multi-omics integration. Findings indicate that AI models for magnetic resonance imaging (MRI) can improve risk stratification and may reduce unnecessary biopsies in some cohorts, particularly when evaluated alongside structured radiology assessment and clinical variables. In digital pathology, deep learning algorithms have shown high agreement with expert genitourinary pathologists for automated Gleason grading in controlled and externally validated settings, with potential to reduce reporting time for high-volume workflows. Additionally, AI-powered liquid biopsy models may support non-invasive risk stratification, particularly for patients with prostate-specific antigen (PSA) levels in the diagnostic gray zone, while multi-omics integration is being investigated to enhance personalized assessment. Despite advances, challenges regarding data heterogeneity, algorithm interpretability, and workflow integration persist. Future research should prioritize multimodal data fusion, explainable AI development, robust calibration and decision-analytic evaluation, and large-scale prospective validation to standardize protocols and fully realize the potential of AI in precision prostate cancer care.
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Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.