ReviewCureus2025
Diagnostic Accuracy of Artificial Intelligence for Detection of Meniscus Pathology on Magnetic Resonance Imaging: A Systematic Review.
Review in Cureus, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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
1 citing paper in PubMed.
- Diagnostic accuracy of MRI for meniscal tear patterns in ACL-deficient knees: comparison with arthroscopic findings.Skeletal radiology · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Meniscus tears can be difficult to diagnose on magnetic resonance imaging (MRI) due to the various types of tears. As artificial intelligence (AI) continues to advance, it could serve as a valuable tool that assists physicians with diagnostic accuracy and efficiency. The purpose of this study was to analyze the performance of AI in diagnosing meniscus tears on MRI using an array of studies and to compare the performance of AI to that of physicians. A literature search was conducted on PubMed and Embase for articles regarding the use of AI for the detection of meniscus tears. AI model, number of MRI studies, sensitivity, specificity, accuracy, AUC, and comparison to physicians encompassed the data that was extracted. A total of 18 studies, comprising 47,621 MRIs, were included in this review. The AUC of these AI models ranged from 0.781 to 0.984, averaging 0.912±0.053. The pooled sensitivity and specificity were 0.831±0.097 and 0.868±0.088, respectively. Three of these studies compared their results to those of 10 radiologists and two orthopedic surgeons. When comparing the sensitivity and specificity of AI models to those of physicians, Cochrane's Q was statistically significant (p < 0.01) with large heterogeneity amongst the two groups (I
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.