Background/Objectives: Chest radiography is the primary first-line imaging tool for diagnosing pneumothorax in pediatric emergency settings. However, interpretation under clinical pressures such as high patient volume may lead to delayed or missed diagnosis, particularly for subtle cases. This study aimed to evaluate the diagnostic performance of ChatGPT-5, a multimodal large language model, in detecting and localizing pneumothorax on pediatric chest radiographs using multiple prompting strategies. Methods: In this retrospective study, 380 pediatric chest radiographs (190 pneumothorax cases and 190 matched controls) from a tertiary hospital were interpreted using ChatGPT-5 with three prompting strategies: instructional, role-based, and clinical-context. Performance metrics, including accuracy, sensitivity, specificity, and conditional side accuracy, were evaluated against an expert-adjudicated reference standard. Results: ChatGPT-5 achieved an overall accuracy of 0.77–0.79 and consistently high specificity (0.96–0.98) across all prompts, with stable reproducibility. However, sensitivity was limited (0.57–0.61) and substantially lower for small pneumothoraces (American College of Chest Physicians ACCP: 0.18–0.22; British Thoracic Society BTS: 0.41–0.46) than for large pneumothoraces (ACCP: 0.75–0.79; BTS: 0.85–0.88). The conditional side accuracy exceeded 0.96 when pneumothorax was correctly detected. No significant differences were observed among prompting strategies. Conclusions: ChatGPT-5 showed consistent but limited diagnostic performance for pediatric pneumothorax. Although the high specificity and reproducible detection of larger pneumothoraces reflect favorable performance characteristics, the unacceptably low sensitivity for subtle pneumothoraces precludes it from independent clinical interpretation and underscores the necessity of oversight by emergency clinicians.
Wang et al. (Sun,) studied this question.