SynthesisProstate cancer and prostatic diseases2023
A systematic review and meta-analysis of artificial intelligence diagnostic accuracy in prostate cancer histology identification and grading.
Synthesis in Prostate cancer and prostatic diseases, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers, 2 of them syntheses that pooled it.
What it found
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Who cites it
29 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- AI-aided diagnostic performance for prostate MRI: systematic review and meta-analysis.Prostate cancer and prostatic diseases · 2026Pooled it
- The Performance of Wearable AI in Detecting Stress Among Students: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2024Pooled it
- Artificial intelligence for detection, grading, and prognostication in prostate cancer pathology: A scoping review.Histology and histopathology · 2026Article
- Explainable artificial intelligence reveals key surgical parameters in robot-assisted and open radical prostatectomy.Scientific reports · 2026Article
- The evolution of prostate cancer grading: from Gleason score to risk taxonomy and the artificial intelligence revolution.Virchows Archiv : an international journal of pathology · 2026Review
- From prostate-specific antigen to precision: The future of prostate cancer diagnosis with artificial intelligence, biomarkers, and imaging.Current urology · 2026Review
- Development of an artificial intelligence-based computer-aided detection system for routine gastric biopsy diagnosis.Journal of pathology informatics · 2026Article
- Modern integrative prostate cancer diagnostics.Current opinion in urology · 2026Review
- Deployment of Artificial Intelligence in Clinical Oral Pathology: Evidence Summary and Implementation Gaps.Analytical cellular pathology (Amsterdam) · 2026Review
- Artificial intelligence in prostate biopsy: diagnostic applications, risk stratification, and precision oncology.Frontiers in oncology · 2026Review
- A generalised vision transformer-based self-supervised model for diagnosing and grading prostate cancer using histological images.Prostate cancer and prostatic diseases · 2025Article
- Artificial Intelligence Across the Prostate Cancer Pathway: Screening, Imaging, Pathology, and Biomarkers.Cureus · 2025Review
- Real-world Evidence on Baseline Characteristics and Treatment in Metastatic Hormone-sensitive Prostate Cancer: Findings from the PIONEER 2.0 Big Data Investigation Group.European urology open science · 2025Article
- Real-world evidence in localized and locally advanced prostate cancer: applying artificial intelligence to electronic health records.BMC cancer · 2025Observational
- Patient satisfaction and decision regret in patients undergoing radical prostatectomy: a multicenter analysis.International urology and nephrology · 2025Article
- Attention-enhanced hybrid U-Net for prostate cancer grading and explainability.Scientific reports · 2025Article
- Beyond PSA: The Future of Prostate Cancer Diagnosis Using Artificial Intelligence, Novel Biomarkers, and Advanced Imagery.Life (Basel, Switzerland) · 2025Review
- Accuracy, readability, and understandability of large language models for prostate cancer information to the public.Prostate cancer and prostatic diseases · 2025Article
- The Role of Artificial Intelligence in the Evaluation of Prostate Pathology.Pathology international · 2025Review
- Quality of information and appropriateness of Open AI outputs for prostate cancer.Prostate cancer and prostatic diseases · 2025Article
Corrections and comments
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Authors and funding
20 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundArtificial intelligence (AI) is a promising tool in pathology, including cancer diagnosis, subtyping, grading, and prognostic prediction.
methodsThe aim of the study is to assess AI application in prostate cancer (PCa) histology. We carried out a systematic literature search in 3 databases. Primary outcome was AI accuracy in differentiating between PCa and benign hyperplasia. Secondary outcomes were AI accuracy in determining Gleason grade and agreement among AI and pathologists.
resultsOur final sample consists of 24 studies conducted from 2007 to 2021. They aggregate data from roughly 8000 cases of prostate biopsy and 458 cases of radical prostatectomy (RP). Sensitivity for PCa diagnostic exceeded 90% and ranged from 87% to 100%, and specificity varied from 68% to 99%. Overall accuracy ranged from 83.7% to 98.3% with AUC reaching 0.99. The meta-analysis using the Mantel-Haenszel method showed pooled sensitivity of 0.96 with I
conclusionsThe accuracy of AI for PCa identification and grading is comparable to expert pathologists. This is a promising approach which has several possible clinical applications resulting in expedite and optimize pathology reports. AI introduction into common practice may be limited by difficult and time-consuming convolutional neural network training and tuning.
Indexed as
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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.