ReviewCancers2025
Improving Early Prostate Cancer Detection Through Artificial Intelligence: Evidence from a Systematic Review.
Review in Cancers, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
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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
4 citing papers in PubMed.
- Artificial intelligence in prostate MRI: Comparative diagnostic performance in a high-prevalence cohort.Acta radiologica (Stockholm, Sweden : 1987) · 2026Article
- Deep Learning Based Computer-Aided Detection of Prostate Cancer Metastases in Bone Scintigraphy: An Experimental Analysis.Journal of imaging · 2026Article
- AI in Prostate Cancer Screening & Diagnosis: A Registry-Based Study of ClinicalTrials.gov Trials.Inquiry : a journal of medical care organization, provision and financingArticle
- Outsmarting Metastatic Prostate Cancer: Integration of Imaging, Liquid Biopsies and Biomarkers With Artificial Intelligence.Technology in cancer research & treatmentReview
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
11 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundProstate cancer is one of the most common malignancies in men and a leading cause of cancer-related mortality. Early detection is essential to ensure curative treatment and favorable outcomes, but traditional diagnostic approaches-such as serum prostate-specific antigen (PSA) testing, digital rectal examination (DRE), and histopathological confirmation following biopsy-are limited by suboptimal accuracy and variability. Multiparametric magnetic resonance imaging (mpMRI) has improved diagnostic performance but remains highly dependent on reader expertise. Artificial intelligence (AI) offers promising opportunities to enhance diagnostic accuracy, reproducibility, and efficiency in prostate cancer detection.
objectiveTo evaluate the diagnostic accuracy and reporting timeliness of AI-based technologies compared with conventional diagnostic methods in the early detection of prostate cancer.
methodsFollowing PRISMA 2020 guidelines, PubMed, Scopus, Web of Science, and Cochrane Library were searched for studies published between January 2015 and April 2025. Eligible designs included randomized controlled trials, cohort, case-control, and pilot studies applying AI-based technologies to early prostate cancer diagnosis. Data on AUC-ROC, sensitivity, specificity, predictive values, diagnostic odds ratio (DOR), and time-to-reporting were narratively synthesized due to heterogeneity. Risk of bias was assessed using the QUADAS-AI tool.
resultsTwenty-three studies involving 23,270 patients were included. AI-based technologies achieved a median AUC-ROC of 0.88 (range 0.70-0.93), with median sensitivity and specificity of 0.86 and 0.83, respectively. Compared with radiologists, AI or AI-assisted readings improved or matched diagnostic accuracy, reduced inter-reader variability, and decreased reporting time by up to 56%.
conclusionsAI-based technologies show strong diagnostic performance in early prostate cancer detection. However, methodological heterogeneity and limited standardization restrict generalizability. Large-scale prospective trials are required to validate clinical integration.
Indexed as
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.