ReviewDiagnostics (Basel, Switzerland)2021
Artificial Intelligence Based Algorithms for Prostate Cancer Classification and Detection on Magnetic Resonance Imaging: A Narrative Review.
Review in Diagnostics (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT06401434 (EVALUATE), which is not on this map. Cited by 60 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.
EVALUATE: Estimating the Value-Add of Lunit Software's Use to Accelerate TB Elimination
Who cites it
60 citing papers in PubMed.
- Present status of prostate cancer diagnosis, limitations, challenges, and future endeavors.Annals of medicine · 2026Review
- Artificial intelligence for detection, grading, and prognostication in prostate cancer pathology: A scoping review.Histology and histopathology · 2026Article
- Use of Artificial Intelligence in prostate MRI: A rapid scoping review highlighting limited evidence in screening context.European journal of radiology · 2026Article
- Toward Autonomous Prostate Cancer Clinical Significance Determination from Spectral/Statistics Features in Bi-Parametric MRI.Cancers · 2026Article
- Artificial intelligence may enhance the role of magnetic resonance imaging in prostate cancer focal therapy.Prostate cancer and prostatic diseases · 2026Review
- Olfactory Science and Technology in Prostate Cancer Diagnosis: From Invertebrate Models to Artificial Intelligence.Life (Basel, Switzerland) · 2026Review
- Combined radiomics, PI-RADS, and clinical model improve significant prostate cancer prediction and guide biopsy decision.Insights into imaging · 2026Article
- From AI-based image analysis to surgical decision support in prostate cancer: interdisciplinary application of the international radiomics platform.Frontiers in oncology · 2026Article
- Simulating workload reduction with an AI-based prostate cancer detection pathway using a prediction uncertainty metric.European radiology · 2025Article
- Improving Early Prostate Cancer Detection Through Artificial Intelligence: Evidence from a Systematic Review.Cancers · 2025Review
- AI-based detection of MRI-invisible prostate cancer with nnU-Net.The Canadian journal of urology · 2025Article
- Artificial Intelligence in Prostate MRI: Current Evidence and Clinical Translation Challenges-A Narrative Review.Journal of imaging · 2025Review
- The Role of Radiomic Analysis and Different Machine Learning Models in Prostate Cancer Diagnosis.Journal of imaging · 2025Article
- AI-Assisted vs Unassisted Identification of Prostate Cancer in Magnetic Resonance Images.JAMA network open · 2025Article
- A Narrative Review of Artificial Intelligence in MRI-Guided Prostate Cancer Diagnosis: Addressing Key Challenges.Diagnostics (Basel, Switzerland) · 2025Review
- Mitigating bias in prostate cancer diagnosis using synthetic data for improved AI driven Gleason grading.NPJ precision oncology · 2025Article
- Assessing Cancer Presence in Prostate MRI Using Multi-Encoder Cross-Attention Networks.Journal of imaging · 2025Article
- Comparing and Combining Artificial Intelligence and Spectral/Statistical Approaches for Elevating Prostate Cancer Assessment in a Biparametric MRI: A Pilot Study.Diagnostics (Basel, Switzerland) · 2025Article
- Improved prostate cancer diagnosis: upgraded prostate imaging reporting and data system (PI-RADS) scores by zoomed diffusion-weighted imaging enhance deep-learning-based computer-aided diagnosis accuracy.Quantitative imaging in medicine and surgery · 2025Article
- Patient perspectives on the use of artificial intelligence in prostate cancer diagnosis on MRI.European radiology · 2025Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
Due to the upfront role of magnetic resonance imaging (MRI) for prostate cancer (PCa) diagnosis, a multitude of artificial intelligence (AI) applications have been suggested to aid in the diagnosis and detection of PCa. In this review, we provide an overview of the current field, including studies between 2018 and February 2021, describing AI algorithms for (1) lesion classification and (2) lesion detection for PCa. Our evaluation of 59 included studies showed that most research has been conducted for the task of PCa lesion classification (66%) followed by PCa lesion detection (34%). Studies showed large heterogeneity in cohort sizes, ranging between 18 to 499 patients (median = 162) combined with different approaches for performance validation. Furthermore, 85% of the studies reported on the stand-alone diagnostic accuracy, whereas 15% demonstrated the impact of AI on diagnostic thinking efficacy, indicating limited proof for the clinical utility of PCa AI applications. In order to introduce AI within the clinical workflow of PCa assessment, robustness and generalizability of AI applications need to be further validated utilizing external validation and clinical workflow experiments.
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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.