ReviewMayo Clinic proceedings. Digital health2026
Artificial Intelligence in Pelvic Fracture Diagnosis and Outcome Prediction: A Systematic Review and Meta-analysis.
Review in Mayo Clinic proceedings. Digital health, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
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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.
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0 citing papers in PubMed.
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Corrections and comments
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Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Objectives: To synthesize the performance of artificial intelligence (AI) applications for detecting pelvic fractures, classifying severity, and predicting clinical outcomes relative to clinicians. Patients and Methods: The study was designed as a systematic review and meta-analysis (PROSPERO CRD420251141768). Ovid Embase, Ovid MEDLINE, PubMed, Scopus, and Cochrane CENTRAL were searched for articles published from database inception to September 11, 2025. Studies were included if they evaluated AI models for pelvic ring fractures in adults using pelvic radiographs. Case series, reviews, and abstracts without full data were excluded. Summary level data were independently extracted using a standardized template. Diagnostic metrics were pooled using a random-effects model. Outcomes included pooled sensitivity, specificity, area under the receiver operating characteristic curve (AUC), and accuracy. Results: Fourteen studies were included. Thirteen studies evaluated radiographic fracture detection or classification (n=31,166 radiographs) and one study evaluated outcome prediction. AI demonstrated high pooled performance: accuracy 0.96 (95% CI, 0.91-0.98; Conclusion: AI algorithms show promise as supportive tools for pelvic fracture detection, achieving diagnostic performance comparable to expert clinicians. However, included studies exhibit substantial heterogeneity, selection bias, and limited external validation. Large-scale, prospective validation is necessary before widespread clinical adoption.
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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.