SynthesisProstate cancer and prostatic diseases2026
Artificial Intelligence (AI)-based tools in the diagnosis and management of prostate cancer: a systematic review and meta-analysis.
Synthesis in Prostate cancer and prostatic diseases, 2026. 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.
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Who cites it
4 citing papers in PubMed.
- Artificial intelligence in prostate cancer care from performance to clinical integration.Prostate cancer and prostatic diseases · 2026Article
- Patients' and Physicians' Perceptions of AI Integration in Prostate Cancer Diagnosis: Mixed Methods Study of Challenges to the Patient-Physician Relationship.Journal of medical Internet research · 2026Article
- Artificial Intelligence for Osteoporosis Diagnosis, Risk Prediction and Therapy: Current Advances, Clinical Challenges, and Future Perspectives.Clinical interventions in aging · 2026Review
- Outsmarting Metastatic Prostate Cancer: Integration of Imaging, Liquid Biopsies and Biomarkers With Artificial Intelligence.Technology in cancer research & treatmentReview
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundRecent advancements in artificial intelligence (AI) hold great promise in oncology, including prostate cancer care. Despite its promises, there is a lack of comprehensive synthesis and knowledge regarding the efficacy of the current AI-based prostate cancer tools. This study aims to identify, evaluate and synthesize the existing evidence on AI-based tools developed for the diagnosis, prognosis, and management of prostate cancer.
methodWe performed a systematic review of published studies from January 2020 to April 2025 that were retrieved from PubMed, Scopus, and Clinical Trials.gov focusing on the AI-based tools that are used in the diagnosis and management of prostate cancer care. Two independent reviewers utilized the PRISMA 2020 guidelines, develop a data charter and synthesize the study data using Covidence Software along with QUADAS-AI tool to assess paper quality and evaluate risk of bias. Meta-analysis was conducted on synthesized data using R.
results43 studies were included, mostly retrospective and diagnostic-focused (n = 29), with deep learning being the most common AI model (49%). A meta-analysis of 34 studies with random effects pooled performance on AUC for the diagnostic tools (k = 27, MD = 0.845, 95% CI: 0.809,0.881), while prognostic tools (k = 7, MD = 0.785, 95% CI: 0.715, 0.856), with subgroup analysis indicating deep learning models (k = 17, MD = 0.854, 95% CI: 0.808, 0.901) out performed classical models (XGBoost, SVM, RF; k = 14, MD = 0.805, 95% CI: 0.756, 0.856). Seven narrative studies highlighted the emerging LLM role, and quality assessment revealed a low risk of bias, though concerns remained on the applicability of tools due to the validation method.
conclusionThis review highlights the promising AI tool performance for prostate cancer care continuum, while concerns on pool performances and real-world applicability. Future studies should emphasize human-centric design with equity-focused evaluations to ensure robust, ethical, scalable AI deployments in prostate cancer care.
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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.