ArticleKorean journal of radiology2025
Accuracy of Large Language Models in Detecting Cases Requiring Immediate Reporting in Pediatric Radiology: A Feasibility Study Using Publicly Available Clinical Vignettes.
Article in Korean journal of radiology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Research on lung nodule detection in X-ray plain films based on improved YOLOv12 model.Scientific reports · 2026Article
- Comparative performance of recent and prior large language models and pediatric residents on pediatric in-training examination questions.Scientific reports · 2026Article
- Diagnostic Accuracy and Stability of Multimodal Large Language Models for Hand Fracture Detection: A Multi-Run Evaluation on Plain Radiographs.Diagnostics (Basel, Switzerland) · 2026Article
- Radiology Research and Publishing Across 2025-2026: Perspectives FromKorean journal of radiology · 2026Article
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Authors and funding
10 authors.
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Abstract
objectiveTo evaluate the accuracy of multimodal large language models (LLMs) in detecting cases requiring immediate radiology reporting in pediatric radiology. MATERIALS AND
methodsSeventy-one publicly available, paraphrased pediatric clinical vignettes with images-sourced from the
resultsLLMs demonstrated 60.6%-83.1% accuracy in detecting cases requiring immediate radiology reporting: 57.7%-81.7% and 53.5%-87.3% for KTAS levels 1-2 and CVR cases, respectively. Gemini-Flash with t1 showed the highest accuracy among the LLMs: 83.1% (95% confidence interval [CI]: 74.6%-91.5%), 81.7% (95% CI: 71.8%-90.1%), and 87.3% (95% CI: 78.9%-94.4%) for identifying cases requiring immediate reporting, KTAS level 1-2 cases, and CVR cases, respectively, despite its low sensitivity for CVR detection (3/11, 27.3%). Human readers demonstrated 62.0%-84.5% accuracy for immediate radiology reporting, 73.2%-84.5% for KTAS levels 1-2, and 39.4%-94.4% for CVR cases. The accuracy of Gemini-Flash t1 in identifying cases requiring immediate radiology reporting was comparable to that of the most accurate human reader (vs. expert pediatrician: 84.5% [95% CI: 76.1%-93.0%];
conclusionMultimodal LLMs may achieve overall accuracy comparable to or exceeding that of human readers in identifying cases requiring immediate radiology reporting, supporting their potential use for pediatric radiology worklist prioritization. However, the models' sensitivity in detecting such cases was not reliable.
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