Evaluation of large language models for providing educational information in orthokeratology care.

Abstract

Background: Large language models (LLMs) are gaining popularity in solving ophthalmic problems. However, their efficacy in patient education regarding orthokeratology, one of the main myopia control strategies, has yet to be determined.

Methods: This cross-sectional study established a question bank consisting of 24 orthokeratology-related questions used as queries for GTP-4, Qwen-72B, and Yi-34B to prompt responses in Chinese. Objective evaluations were conducted using an online platform. Subjective evaluations including correctness, relevance, readability, applicability, safety, clarity, helpfulness, and satisfaction were performed by experienced ophthalmologists and parents of myopic children using a 5-point Likert scale. The overall standardized scores were also calculated.

Results: The word count of the responses from Qwen-72B (199.42 ± 76.82) was the lowest (P < 0.001), with no significant differences in recommended age among the LLMs. GPT-4 (3.79 ± 1.03) scored lower in readability than Yi-34B (4.65 ± 0.51) and Qwen-72B (4.65 ± 0.61) (P < 0.001). No significant differences in safety, relevance, correctness, and applicability were observed across the three LLMs. Parental evaluations rated all LLMs an average score exceeding 4.7 points, with GPT-4 outperforming the others in helpfulness (P = 0.004) and satisfaction (P = 0.016). Qwen-72B's overall standardized scores surpassed those of the other two LLMs (P = 0.048).

Conclusions: GPT-4 and the Chinese LLM Qwen-72B produced accurate and beneficial responses to inquiries on orthokeratology. Further enhancement to bolster precision is essential, particularly within diverse linguistic contexts.