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Background <p>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.</p> Method <p>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.</p> Results <p>43 studies were included, mostly retrospective and diagnostic-focused (<i>n</i> = 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.</p> Conclusion <p>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.</p>

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Artificial Intelligence (AI)-based tools in the diagnosis and management of prostate cancer: a systematic review and meta-analysis

  • 期刊论文

  • 发表日期：

    2025年12月12日

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  • Hein Minn Tun,
  • Lin Naing,
  • Owais Ahmed Malik,
  • Hanif Abdul Rahman

摘要

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.