Accuracy of ChatGPT, Gemini, Copilot, and Claude to Blepharoplasty-Related Questions.

[BACKGROUND] This study aimed to evaluate the performance of four large language models (LLMs)-ChatGPT, Gemini, Copilot, and Claude-in responding to upper eyelid blepharoplasty-related questions, focusing on medical accuracy, clinical relevance, response length, and readability.

[METHODS] A set of queries regarding upper eyelid blepharoplasty, covering six categories (anatomy, surgical procedure, additional intraoperative procedures, postoperative monitoring, follow-up, and postoperative complications) were posed to each LLM. An identical prompt establishing clinical context was provided before each question. Responses were evaluated by three ophthalmologists using a 5-point Likert scale for medical accuracy and a 3-point Likert scale for clinical relevance. The length of the responses was assessed. Readability was also evaluated using the Flesch Reading Ease Score, Flesch-Kincaid Grade Level, Coleman-Liau Index, Gunning Fog Index, and Simple Measure of Gobbledygook grade.

[RESULTS] A total of 30 standardized questions were presented to each LLM. None of the responses from any LLM received a score of 1 regarding medical accuracy for any question. ChatGPT achieved an 80% 'highly accurate' response rate, followed by Claude (60%), Gemini (40%), and Copilot (20%). None of the responses from ChatGPT and Claude received a score of 1 regarding clinical relevance, whereas 10% of Gemini's responses and 26.7% of Copilot's responses received a score of 1. ChatGPT also provided the most clinically 'relevant' responses (86.7%), outperforming the other LLMs. Copilot generated the shortest responses, while ChatGPT generated the longest. Readability analyses revealed that all responses required advanced reading skills at a 'college graduate' level or higher, with Copilot's responses being the most complex.

[CONCLUSION] ChatGPT demonstrated superior performance in both medical accuracy and clinical relevance among evaluated LLMs regarding upper eyelid blepharoplasty, particularly excelling in postoperative monitoring and follow-up categories. While all models generated complex texts requiring advanced literacy, ChatGPT's detailed responses offer valuable guidance for ophthalmologists managing upper eyelid blepharoplasty cases.

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