Evaluation of the reliability and readability of answers given by chatbots to frequently asked questions about endophthalmitis: A cross-sectional study on chatbots

<b>Objective:</b> This study aimed to investigate the accuracy, reliability, and readability of A-Eye Consult, ChatGPT-4.0, Google Gemini and Copilot AI large language models (LLMs) in responding to patient questions about endophthalmitis. <b>Methods:</b> The LLMs' responses to 25 questions about endophthalmitis, frequently asked by patients, were evaluated by two ophthalmologists using a five-point Likert scale, with scores ranging from 1-5. The DISCERN scale assessed the reliability of the LLMs' responses, whereas the Flesch Reading Ease (FRE) and Flesch-Kincaid Grade Level (FKGL) indices assessed readability and text complexity, respectively. <b>Results:</b> A-Eye Consult and ChatGPT-4.0 outperformed Google Gemini and Copilot in providing comprehensive and precise responses. The Likert score significantly differed across all four LLMs (<i>p</i> < .001), with A-Eye Consult scoring significantly higher than Google Gemini and Copilot (<i>p</i> < .001). <b>Conclusions:</b> A-Eye Consult and ChatGPT-4.0 responses, while more complex than those of other LLMs, provided more reliable and accurate information.