Diagnostic Performance of a Large Language Model ( <scp>ChatGPT</scp> ‐4o) in Chronic Rhinosinusitis <scp>CT</scp> Scan Interpretation

Background: Large language models (LLMs), such as ChatGPT, are increasingly utilized by physicians for clinical decision support due to their ease of use and versatility. However, their performance in diagnostic imaging remains largely untested. This study prospectively evaluates ChatGPT's ability to interpret sinus computed tomography (CT) scans for chronic rhinosinusitis (CRS), using radiologist assessment as the reference standard. Methods: In this prospective cohort study, 102 coronal sinus CT scans were evaluated by both a board-certified radiologist and ChatGPT-4o. Each scan was screen recorded and uploaded twice to ChatGPT to assess repeatability, resulting in 306 total interpretations. The radiologist reviewed the same screen recordings provided to ChatGPT. Both raters assessed 11 predefined binary anatomical features and generated Lund-Mackay scores. Diagnostic performance was assessed using standard accuracy metrics, and inter-rater agreement was evaluated using established reliability coefficients. Results: = 0.11), and agreement with the radiologist was poor (ICC < 0.07). Conclusion: ChatGPT demonstrates partial capability in identifying specific sinus CT findings; however, it lacks overall diagnostic consistency. Human radiologists remain essential, and the clinical use of LLMs in imaging should be approached with caution.