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Diagnostic Performance of ChatGPT-5 for Detecting Pediatric Pneumothorax on Chest Radiographs: A Multi-Prompt
Chih-Hao Wang1, Po-Chih Lin1, Shin-Lin Shih2,3
1Department of Pediatrics, MacKay Children's Hospital, Taipei 104217, Taiwan.
ChatGPT-5 shows promise in detecting pneumothorax on pediatric chest X-rays, with high specificity but limited sensitivity for subtle cases. Further clinical oversight is essential for accurate diagnosis.
Area of Science:
- Artificial Intelligence in Medical Imaging
- Pediatric Radiology
- Diagnostic Performance Evaluation
Background:
- Chest radiography is crucial for diagnosing pediatric pneumothorax in emergency settings.
- Clinical pressures can lead to missed or delayed diagnoses of subtle pneumothorax.
- Evaluating AI tools like ChatGPT-5 for diagnostic assistance is important.
Purpose of the Study:
- To assess ChatGPT-5's diagnostic performance in detecting and localizing pneumothorax on pediatric chest radiographs.
- To compare different prompting strategies for AI interpretation.
- To determine the AI's accuracy, sensitivity, and specificity.
Main Methods:
- Retrospective analysis of 380 pediatric chest radiographs (190 pneumothorax, 190 controls).
- ChatGPT-5 interpreted images using instructional, role-based, and clinical-context prompts.
- Performance metrics evaluated against expert-adjudicated standards.
Main Results:
- ChatGPT-5 achieved 77-79% accuracy and high specificity (96-98%).
- Sensitivity was limited (57-61%), particularly for small pneumothoraces (18-46%).
- Detection of large pneumothoraces was better (75-88%), with high side accuracy (>96%).
Conclusions:
- ChatGPT-5 demonstrates consistent but limited diagnostic capability for pediatric pneumothorax.
- High specificity and detection of large pneumothoraces are strengths.
- Low sensitivity for subtle cases necessitates clinician oversight, precluding independent use.
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