Evaluation and comparison of large language models’ responses to questions related optic neuritis

Abstract

Objectives: Large language models (LLMs) show promise as clinical consultation tools and may assist optic neuritis patients, though research on their performance in this area is limited. Our study aims to assess and compare the performance of four commonly used LLM-Chatbots-Claude-2, ChatGPT-3.5, ChatGPT-4.0, and Google Bard-in addressing questions related to optic neuritis. Methods: We curated 24 optic neuritis-related questions and had three ophthalmologists rate the responses on two three-point scales for accuracy and comprehensiveness. We also assessed readability using four scales. The final results showed performance differences among the four LLM-Chatbots. Results: = 0.1531). Note that all responses require at least a university-level reading proficiency. Conclusion: Large language models-Chatbots hold immense potential as clinical consultation tools for optic neuritis, but they require further refinement and proper evaluation strategies before deployment to ensure reliable and accurate performance.

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