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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)Cite this article

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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.

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Fig. 1
Fig. 2: Forest plot for subgroup analysis of Meta regression of AUC for included studies (n = 34).
Fig. 3

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Data availability

All data generated or analyzed during this study is included in this published article and its supplementary information files. No new datasets were generated, as this is a systematic review and meta-analysis of publicly available literature.

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Authors and Affiliations

  1. PAPRSB Institute of Health Sciences, Universiti Brunei Darussalam, Bandar, Seri Begawan, Brunei Darussalam

    Hein Minn Tun, Lin Naing & Hanif Abdul Rahman

  2. School of Digital Science, Universiti Brunei Darussalam, Bandar, Seri Begawan, Brunei Darussalam

    Hein Minn Tun & Owais Ahmed Malik

  3. Saw Swee Hock School of Public Health, National University of Singapore, Singapore, Singapore

    Hanif Abdul Rahman

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Contributions

HMT was responsible for conceptualization, data curation, formal analysis, investigation, methodology, resources, software, validation, visualization, and writing the original draft. LN contributed through supervision and data validation. OAM provided supervision, data validation, and contributed to writing, reviewing, and editing the manuscript. HAR was involved in methodology, formal analysis, supervision, data validation, and writing, reviewing, and editing the manuscript.

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Correspondence to Hein Minn Tun.

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Tun, H.M., Naing, L., Malik, O.A. et al. Artificial Intelligence (AI)-based tools in the diagnosis and management of prostate cancer: a systematic review and meta-analysis. Prostate Cancer Prostatic Dis (2025). https://doi.org/10.1038/s41391-025-01060-w

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