ReviewNature reviews. Urology2019
A new era: artificial intelligence and machine learning in prostate cancer.
Review in Nature reviews. Urology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 212 papers, 9 of them syntheses that pooled it.
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
212 citing papers in PubMed, 9 syntheses or guidelines pooled it.
- AI-aided diagnostic performance for prostate MRI: systematic review and meta-analysis.Prostate cancer and prostatic diseases · 2026Pooled it
- Research progress on risk prediction models for the diabetic foot.Acta diabetologica · 2025Pooled it
- Identification of Factors Affecting Prostate Cancer Using Machine Learning Methods: A Systematic Review.Asian Pacific journal of cancer prevention : APJCP · 2025Pooled it
- Algorithms on the rise: a machine learning-driven survey of prostate cancer literature.Frontiers in oncology · 2025Pooled it
- Predicting criminal and violent outcomes in psychiatry: a meta-analysis of diagnostic accuracy.Translational psychiatry · 2022Pooled it
- The promising role of new molecular biomarkers in prostate cancer: from coding and non-coding genes to artificial intelligence approaches.Prostate cancer and prostatic diseases · 2022Pooled it
- Radiomics and artificial intelligence in prostate cancer: new tools for molecular hybrid imaging and theragnostics.European radiology experimental · 2022Pooled it
- Radiomic and Genomic Machine Learning Method Performance for Prostate Cancer Diagnosis: Systematic Literature Review.Journal of medical Internet research · 2021Pooled it
- Deep Learning in Radiation Oncology Treatment Planning for Prostate Cancer: A Systematic Review.Journal of medical systems · 2020Pooled it
- Automatic ganglion cell detection for improving the efficiency and accuracy of hirschprung disease diagnosis.Scientific reports · 2021Trial
- Navigating Uncertainty in MRI Diagnosis: A Human-AI Collaborative Strategy for Stratifying Clinically Significant Prostate Cancer.Journal of magnetic resonance imaging : JMRI · 2026Article
- Artificial intelligence in urology: A review of United States Food and Drug Administration-cleared devices.BJU international · 2026Review
- Multimodal artificial intelligence in prostate cancer: integrating multiparametric MRI with clinicopathological, molecular, and functional imaging data.Abdominal radiology (New York) · 2026Review
- Big Data Analytics in Urologic Oncology: A Comprehensive Review of Large-scale Database Research and Clinical Applications.Current urology reports · 2026Review
- Accelerating the implementation of digital pathology in India.Journal of pathology informatics · 2026Review
- Reproducibility of natural-language-driven statistical workflows compared with conventional software for diagnostic metrics in prostate cancer research.Investigative and clinical urology · 2026Article
- Machine learning-driven evaluation of protein kinase D3 as a co-diagnostic biomarker in hepatocellular carcinoma.Journal of Zhejiang University. Science. B · 2026Article
- Learning continuous activation fields from microscopic SEM images of lycoperdioid fungi via CNN-guided neural operator modeling.Scientific reports · 2026Article
- Nanostructured lipid carriers as co-delivery systems for cancer therapy: Prospects and challenges.International journal of pharmaceutics: X · 2026Review
- From Spreadsheet To Prediction Tool: A Practical Artificial Intelligence Guide For Urologists.Cureus · 2026Article
152 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Artificial intelligence (AI) - the ability of a machine to perform cognitive tasks to achieve a particular goal based on provided data - is revolutionizing and reshaping our health-care systems. The current availability of ever-increasing computational power, highly developed pattern recognition algorithms and advanced image processing software working at very high speeds has led to the emergence of computer-based systems that are trained to perform complex tasks in bioinformatics, medical imaging and medical robotics. Accessibility to 'big data' enables the 'cognitive' computer to scan billions of bits of unstructured information, extract the relevant information and recognize complex patterns with increasing confidence. Computer-based decision-support systems based on machine learning (ML) have the potential to revolutionize medicine by performing complex tasks that are currently assigned to specialists to improve diagnostic accuracy, increase efficiency of throughputs, improve clinical workflow, decrease human resource costs and improve treatment choices. These characteristics could be especially helpful in the management of prostate cancer, with growing applications in diagnostic imaging, surgical interventions, skills training and assessment, digital pathology and genomics. Medicine must adapt to this changing world, and urologists, oncologists, radiologists and pathologists, as high-volume users of imaging and pathology, need to understand this burgeoning science and acknowledge that the development of highly accurate AI-based decision-support applications of ML will require collaboration between data scientists, computer researchers and engineers.
Indexed as
Identifiers
31092914What 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.