Evidence map›Paper›PMID 40619793›Full record

ReviewBalkan medical journal2025

Generative Artificial Intelligence in Prostate Cancer Imaging.

Fahmida Haque, Benjamin D Simon, Kutsev B Özyörük, Stephanie A Harmon, Barış Türkbey

Abstract readReview
In one paragraph

Review in Balkan medical journal, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from 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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

4 citing papers in PubMed.

  1. Article
  2. Review
  3. Article
  4. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Fahmida HaqueMolecular Imaging Branch, National Cancer Institute, National Institutes of Health, Bethesda, USA.ORCID 0000-0003-2023-2258
Benjamin D SimonMolecular Imaging Branch, National Cancer Institute, National Institutes of Health, Bethesda, USA.ORCID 0000-0002-8658-9711
Kutsev B ÖzyörükMolecular Imaging Branch, National Cancer Institute, National Institutes of Health, Bethesda, USA.ORCID 0000-0001-5943-7440
Stephanie A HarmonMolecular Imaging Branch, National Cancer Institute, National Institutes of Health, Bethesda, USA.ORCID 0000-0002-2507-2399
Barış TürkbeyMolecular Imaging Branch, National Cancer Institute, National Institutes of Health, Bethesda, USA.ORCID 0000-0003-0853-6494

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Prostate cancer (PCa) is the second most common cancer in men and has a significant health and social burden, necessitating advances in early detection, prognosis, and treatment strategies. Improvement in medical imaging has significantly impacted early PCa detection, characterization, and treatment planning. However, with an increasing number of patients with PCa and comparatively fewer PCa imaging experts, interpreting large numbers of imaging data is burdensome, time-consuming, and prone to variability among experts. With the revolutionary advances of artificial intelligence (AI) in medical imaging, image interpretation tasks are becoming easier and exhibit the potential to reduce the workload on physicians. Generative AI (GenAI) is a recently popular sub-domain of AI that creates new data instances, often to resemble patterns and characteristics of the real data. This new field of AI has shown significant potential for generating synthetic medical images with diverse and clinically relevant information. In this narrative review, we discuss the basic concepts of GenAI and cover the recent application of GenAI in the PCa imaging domain. This review will help the readers understand where the PCa research community stands in terms of various medical image applications like generating multi-modal synthetic images, image quality improvement, PCa detection, classification, and digital pathology image generation. We also address the current safety concerns, limitations, and challenges of GenAI for technical and clinical adaptation, as well as the limitations of current literature, potential solutions, and future directions with GenAI for the PCa community.

Indexed as

Artificial IntelligenceDiagnostic ImagingProstatic NeoplasmsGenerative Artificial IntelligenceHumansMale

Identifiers

PMID40619793
PMCPMC12240228

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

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.