aceArtificial intelligence in the radiologic diagnosis of major urological cancers: a meta-analysis
Objective Given the pivotal role of imaging in diagnosing urological cancers, artificial intelligence (AI) has emerged as a promising tool to improve diagnostic accuracy and reliability. This study systematically evaluates the diagnostic performance of AI models in radiologic imaging of urological cancers. Methods A systematic search was conducted in four electronic databases up to June 2026 to identify studies that applied AI algorithms for the diagnosis of urological cancers using CT, MRI, or ultrasound. Eligi…
AI models for CT, MRI, and ultrasound diagnosis of urological cancers achieved higher pooled specificity and AUC than clinicians in a 110-study meta-analysis.
In prostate cancer and MRI subgroups, AI models showed lower sensitivity than clinicians, indicating inconsistent advantage across tasks.
Findings are based on retrospective pooled studies with heterogeneity, requiring further prospective validation before clinical adoption.
Evidence
- Peer-reviewedWorld Journal of Urology2026-08-03
How should this claim be treated?
Truvace Impact Record TRV-2026-0640, v1: “Artificial intelligence in the radiologic diagnosis of major urological cancers: a meta-analysis.” Truvace, 2026-08-04. /record/TRV-2026-0640 (accessed at citation time). sha256 101c1a3226150ba5…
Calibration history
Every change to this record since certification, in the open. None yet — the reading has held since it entered the record.
Certified into the record
How to verify without trusting this page
Fetch the canonical text of any version from /api/record/TRV-2026-0640 and hash it yourself — for example shasum -a 256 on the saved canonical field. The result must equal content_hash, and each version’s text ends with prev:followed by the prior version’s hash (version 1 chains to 64 zeros). If a single character of any version had been altered since certification, the chain would not reproduce.