ReviewFrontiers in immunology2025
Utilization of artificial intelligence in prostate cancer detection: a comprehensive review of innovations in screening and diagnosis.
Review in Frontiers in immunology, 2025. 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
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
4 citing papers in PubMed.
- Present status of prostate cancer diagnosis, limitations, challenges, and future endeavors.Annals of medicine · 2026Review
- Comprehensive biochemical insights into derivatives of PSA and glycosylation-specific changes in PSA: role in diagnosis and management of prostate cancer.Clinical proteomics · 2026Review
- Review
- Outsmarting Metastatic Prostate Cancer: Integration of Imaging, Liquid Biopsies and Biomarkers With Artificial Intelligence.Technology in cancer research & treatmentReview
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Prostate cancer management has long been challenged by the limitations of traditional screening tools like PSA testing, which contribute to significant rates of overdiagnosis and overtreatment. While advanced imaging such as multiparametric MRI (mpMRI) has improved the diagnostic pathway, the integration of Artificial Intelligence (AI) is now catalyzing a paradigm shift across the entire continuum of care. This comprehensive review details the transformative role of AI in prostate cancer. In diagnostics, deep learning algorithms enhance the interpretation of mpMRI by improving lesion detection, segmentation, and risk stratification, thereby reducing unnecessary biopsies. In digital pathology, AI provides automated and consistent Gleason grading, minimizing inter-observer variability and refining prognostication. In the therapeutic domain, AI is crucial for personalizing treatment by streamlining radiotherapy planning through automated contouring, predicting patient outcomes and toxicity, and enabling the development of adaptive therapy strategies for advanced disease. Multimodal AI models that synthesize imaging, biomarker, and clinical data are creating robust predictive tools for superior clinical decision support. Despite formidable challenges related to prospective validation, data equity, and regulatory approval, AI is paving the way for a new standard of care characterized by greater precision, efficiency, and personalization.
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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.