ChatGPT-4 Vision: a promising tool for diagnosing thyroid nodules

Abstract

Objective: This study aims to evaluate the application of ChatGPT-4 Vision in the ultrasonic image analysis of thyroid nodules by comparing its diagnostic efficacy and consistency with those of sonographers. Methods: In this prospective study, conducted in real clinical scenarios, we included 124 patients with pathologically confirmed thyroid nodules who underwent ultrasound examinations at Fujian Medical University Affiliated Second Hospital. A physician, not involved in the study, collected three ultrasound images for each nodule: the maximum cross-sectional, maximum longitudinal, and the section best representing the nodular characteristics. The images were analyzed by the primed ChatGPT-4 Vision and classified according to the 2020 Chinese Guidelines for Ultrasound Malignancy Risk Stratification of Thyroid Nodules (C-TIRADS). Two sonographers with different qualifications (a resident physician and an attending physician) used the same images to classify the nodules according to the C-TIRADS guidelines. Using fine needle aspiration (FNA) biopsy or surgical pathology results as the gold standard, we compared the consistency and diagnostic efficacy of the primed ChatGPT-4 Vision with those of the sonographers. Results: < 0.05). (2) The primed ChatGPT-4 Vision demonstrated good consistency with the resident in thyroid nodule classification (Kappa value = 0.729), though its consistency with the pathological diagnosis was lower than that of the attending physician (Kappa values of 0.457 vs. 0.816, respectively). Conclusion: The primed ChatGPT-4 Vision demonstrates promising clinical utility in thyroid nodule risk stratification, achieving diagnostic performance comparable to resident physicians. Its ability to standardize image analysis aligns with precision medicine goals, offering a foundation for future integration with dynamic ultrasound modalities to enhance pathological correlation.

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