ReviewResearch and reports in urology2021
A Systematic Review of Artificial Intelligence in Prostate Cancer.
Review in Research and reports in urology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 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
30 citing papers in PubMed.
- An artificial intelligence model integrating clinico-laboratory data and single nucleotide polymorphism-based genomic risk for prostate cancer diagnosis in Korean men.Investigative and clinical urology · 2026Article
- PICO-based assessment and categorization of evidence for digital health interventions: an inductive framework development.Frontiers in digital health · 2026Review
- A Multi-Modal Transfer Learning Framework to Reduce Health Disparities in Prostate Adenocarcinoma.bioRxiv : the preprint server for biology · 2025Article
- Artificial Intelligence Across the Prostate Cancer Pathway: Screening, Imaging, Pathology, and Biomarkers.Cureus · 2025Review
- Improving Early Prostate Cancer Detection Through Artificial Intelligence: Evidence from a Systematic Review.Cancers · 2025Review
- Prediction of adverse pathology in prostate cancer using a multimodal deep learning approach based on [European journal of nuclear medicine and molecular imaging · 2025Article
- Mitigating bias in prostate cancer diagnosis using synthetic data for improved AI driven Gleason grading.NPJ precision oncology · 2025Article
- Artificial intelligence utilization in cancer screening program across ASEAN: a scoping review.BMC cancer · 2025Article
- Multimodal approach to optimize biopsy decision-making for PI-RADS 3 lesions on multiparametric MRI.Clinical imaging · 2025Article
- Systematic Review of AI-Assisted MRI in Prostate Cancer Diagnosis: Enhancing Accuracy Through Second Opinion Tools.Diagnostics (Basel, Switzerland) · 2024Review
- AtPCa-Net: anatomical-aware prostate cancer detection network on multi-parametric MRI.Scientific reports · 2024Article
- Identification and validation of an individualized metabolic prognostic signature for predicting the biochemical recurrence of prostate cancer based on the immune microenvironment.European journal of medical research · 2024Article
- Existing trends and applications of artificial intelligence in urothelial cancer A scoping review.Canadian Urological Association journal = Journal de l'Association des urologues du Canada · 2023Article
- Exploring the Use of Artificial Intelligence and Robotics in Prostate Cancer Management.Cureus · 2023Article
- Article
- Use of artificial intelligence in discerning the need for prostate biopsy and readiness for clinical practice: a systematic review protocol.Systematic reviews · 2023Article
- Textural Features of MR Images Correlate with an Increased Risk of Clinically Significant Cancer in Patients with High PSA Levels.Journal of clinical medicine · 2023Article
- The Evidence for Using Artificial Intelligence to Enhance Prostate Cancer MR Imaging.Current oncology reports · 2023Review
- Application of Machine Learning for Differentiating Bone Malignancy on Imaging: A Systematic Review.Cancers · 2023Review
- Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The diagnosis and management of prostate cancer involves the interpretation of data from multiple modalities to aid in decision making. Tools like PSA levels, MRI guided biopsies, genomic biomarkers, and Gleason grading are used to diagnose, risk stratify, and then monitor patients during respective follow-ups. Nevertheless, diagnosis tracking and subsequent risk stratification often lend itself to significant subjectivity. Artificial intelligence (AI) can allow clinicians to recognize difficult relationships and manage enormous data sets, which is a task that is both extraordinarily difficult and time consuming for humans. By using AI algorithms and reducing the level of subjectivity, it is possible to use fewer resources while improving the overall efficiency and accuracy in prostate cancer diagnosis and management. Thus, this systematic review focuses on analyzing advancements in AI-based artificial neural networks (ANN) and their current role in prostate cancer diagnosis and management.
Indexed as
Identifiers
What OpenQuestion holds
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.