Evaluating ChatGPT's Accuracy and Readability in Responding to Common Ophthalmology Questions.

Objectives: Eye-related conditions are a prevalent issue that continues to grow worldwide, affecting the sight of at least 2.2 billion individuals globally. Many patients may have questions or concerns that they bring to the internet before their healthcare provider, which can impact their health behavior. With the popularity of large language model (LLM)-based artificial intelligence (AI) chat platforms, like ChatGPT, there needs to be a better understanding of the suitability of their generated content. We aim to evaluate ChatGPT for the accuracy, comprehensiveness, and readability of its responses to ophthalmology-related medical inquiries.

Methodology: Twenty-two ophthalmology patient questions were generated based on commonly searched symptoms on Google Trends and used as inputs on ChatGPT. Flesch Reading Ease (FRE) and Flesch-Kincaid Grade Level (FKGL) formulas were used to evaluate response readability. Two English-speaking, board-certified ophthalmologists evaluated the accuracy, comprehensiveness, and clarity of the responses as proxies for appropriateness. Other validated tools, including QUEST, DISCERN, and an urgency scale, were used for additional quality metrics. Responses were analyzed using descriptive statistics and comparative tests.  Results: All responses scored a 2.0 for QUEST Tone and 1.0 for Complementarity. DISCERN Uncertainty had a mean of 3.86 ± 0.48, with no responses receiving a 5. Urgency to seek care scores averaged 2.45 ± 0.60, with only the narrow-angle glaucoma response prompting an ambulance call. Readability scores resulted in a mean FRE of 45.3 ± 9.98 and FKGL of 10.1 ± 1.74. These quality assessment scores showed no significant differences between categories of conditions. The ophthalmologists' reviews rated 15/22 (68.18%) of responses as appropriate. The mean scores for accuracy, comprehensiveness, and clarity were 4.41 ± 0.73, 4.89 ± 0.32, and 4.55 ± 0.63, respectively, with comprehensiveness ranking significantly higher than the other aspects (< 0.01). The responses for glaucoma and cataract had the lowest appropriateness ratings.

Conclusions: ChatGPT generally demonstrated appropriate responses to common ophthalmology questions, with high ratings for comprehensiveness, clarity, and support for medical professional follow-up. Performance did vary by conditions, with weaker appropriateness in responses related to glaucoma and cataract.