Accuracy of Large Language Models in Detecting Cases Requiring Immediate Reporting in Pediatric Radiology: A Feasibility Study Using Publicly Available Clinical Vignettes

To evaluate the accuracy of multimodal large language models (LLMs) in detecting cases requiring immediate radiology reporting in pediatric radiology. Seventy-one publicly available, paraphrased pediatric clinical vignettes with images-sourced from the New England Journal of Medicine, The Lancet, Archives of Pediatrics t0 and t1) and four human readers (an expert pediatric radiologist, a trainee radiologist, an expert pediatrician, and a trainee pediatrician). Cases were classified as requiring immediate reporting (n = 33) if they corresponded to Korean Triage and Acuity Scale (KTAS) levels 1-2 (n = 24) or met the criteria for a critical value report (CVR) (n = 11). The most accurate LLM was compared with each human reader, with significance set at P < 0.013. LLMs 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%; P < 0.99). Multimodal 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.