Comparison Between Oculoplastic Surgeons and Two Large Language Models in the Nonsurgical Management of Thyroid Eye Disease: A Questionnaire-Based Cross-Sectional Study.

Background: Thyroid eye disease (TED) presents heterogeneous clinical manifestations requiring nuanced, individualized management. Large language models (LLMs) have shown potential in clinical reasoning, but their consistency with physician decision-making in nonsurgical TED management remains unclear. Methods: A structured 19-item questionnaire covering medical, injection-based, and radiotherapeutic treatments for TED was distributed to 17 oculoplastic surgeons across the Asia-Pacific region. Responses were binarized (1 = selected, 0 = not selected) and compared with standardized outputs from GPT and Gemini. Jaccard and cosine similarity indices were calculated to quantify the alignment between physicians and LLMs. Results: Overall, GPT and Gemini demonstrated comparable alignment with clinicians' treatment preferences. Both models closely matched physician consensus regarding glucocorticoid use and radiotherapy indications but diverged in immunosuppressant selection and injection-based strategies. Similarity was generally consistent across physician experience and patient-volume subgroups. Conclusions: GPT and Gemini demonstrated moderate agreement with participating oculoplastic surgeons in structured nonsurgical TED management scenarios. These findings support further evaluation of LLM-assisted applications within controlled clinical validation frameworks, and additional validation using guideline-based benchmarks and real-world clinical scenarios is required.