Artificial Intelligence (AI)-based tools in the diagnosis and management of prostate cancer: a systematic review and meta-analysis.
[BACKGROUND] Recent advancements in artificial intelligence (AI) hold great promise in oncology, including prostate cancer care.
- 표본수 (n) 29
- 연구 설계 systematic review
APA
Tun HM, Naing L, et al. (2025). Artificial Intelligence (AI)-based tools in the diagnosis and management of prostate cancer: a systematic review and meta-analysis.. Prostate cancer and prostatic diseases. https://doi.org/10.1038/s41391-025-01060-w
MLA
Tun HM, et al.. "Artificial Intelligence (AI)-based tools in the diagnosis and management of prostate cancer: a systematic review and meta-analysis.." Prostate cancer and prostatic diseases, 2025.
PMID
41388186
Abstract
[BACKGROUND] Recent 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.
[METHOD] We 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.
[RESULTS] 43 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.
[CONCLUSION] This 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.
[METHOD] We 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.
[RESULTS] 43 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.
[CONCLUSION] This 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.