ArticleNPJ digital medicine2025
Comparative analysis of AI support levels in clinical interpretation of traumatic pelvic radiographs.
Article in NPJ digital medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
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Who cites it
4 citing papers in PubMed.
- Between Accessibility and Reliability: High Confidence, Low Control in General-Purpose Multimodal Models for Hip Fracture Radiograph Interpretation.Journal of clinical medicine · 2026Article
- AI-Assisted Fracture Detection in Orthopedic and Trauma Imaging: Where It Works, Where It Fails, and Principles for Safe Clinical Deployment.Diagnostics (Basel, Switzerland) · 2026Review
- Artificial intelligence-assisted training for rib fracture interpretation: a prospective study in undergraduate medical students.World journal of emergency surgery : WJES · 2026Observational
- Evaluation framework for assessing the quality and safety of artificial intelligence solutions for the acute care surgeon: a narrative review from the Eastern Association for the Surgery of Trauma quality, safety, and outcomes committee.Trauma surgery & acute care open · 2026Review
Corrections and comments
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Authors and funding
9 authors.
Funding
Abstract
Plain pelvic radiographs (PXR) remain crucial for initial trauma assessment, yet interpretation challenges persist. While artificial intelligence (AI) shows promise, its practical impact across specialties remains unexplored. We conducted a retrospective image-based, multi-reader multi-case (MRMC) study using a standardized, prospectively planned evaluation protocol. A total of 26 physicians (8 radiologists, 10 emergency physicians, 8 trauma surgeons) interpreted 150 PXRs in three sequential sessions: without AI, with AI-alert, and with AI-visual guidance. AI assistance improved overall diagnostic accuracy from 0.870 to 0.940 (p < 0.001) and reduced interpretation time from 22.70 to 9.58 s (p < 0.001). Non-radiologists showed substantial improvements, with emergency physicians demonstrating increases in specificity (26.2%, p = 0.006) and positive predictive value (41.5%, p = 0.006). Trauma surgeons with AI-visual guidance achieved comparable accuracy to unaided radiologists (0.940 vs. 0.920, p = 0.556). Tailored AI assistance effectively bridges the performance gap between radiologists and non-radiologists while reducing reading time. These findings suggest AI integration could enhance clinical workflow efficiency across specialties in trauma care settings.
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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.