Year: 2025
Type: article
Abstract: . A pilot study evaluated the capabilities of the universal multimodal LLM ChatGPT 03 for interpreting otoscopic images. Thirty-eight frames were grouped into nine clinical categories—from normal and foreign bodies to postoperative states and middle-ear tumors. A “gold standard” annotation was provided by two otorhinolaryngology experts (Cohen’s κ > 0.85), with consensus reached in cases of disagreement. Each frame was processed in a new session with the prompt “What do you see in this photo?” ChatGPT 03 achieved 100% accuracy in distinguishing “normal vs. pathology” (95% CI 90.8–100%), with sensitivity and specificity, PPV/NPV (positive predictive value/negative predictive value) = 100%. The correctness of its clinical diagnosis formulation was 81.6% (31/38). For five key morphological features (perforation, effusion, hyperemia, tympanosclerosis, cholesteatoma), the mean F1-score was 0.92, and Cohen’s κ = 0.87. Expert ratings of the utility of its text descriptions on a 5-point scale yielded M = 4.4 ± 0.6, ICC = 0.82, with no significant differences between groups (p = 0.24). Spearman’s ρ = 0.72 (p < 0.001) confirmed a strong positive correlation between the number of correctly identified features and the usefulness assessment. The average response time was 30–40 s. These findings underscore ChatGPT 03’s high potential for preliminary screening, report standardization, and education. Clinical implementation will require large-scale prospective validation, structured output, and integration of quantitative tools.
Source: Russian otorhinolaryngology
Authors: M. V. Komarov, O. I. Goncharov, A. A. Fedotova
Language: English
Subfield: Health Informatics
Field: Medicine
Domain: Health Sciences
Open Access status: bronze
OpenAlex ID: W4416307337