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Most cases of optic neuritis (ON) occur in women and in patients between the ages of 15 and 45 years, which represents a key demographic of individuals who seek health information using the internet. As clinical providers strive to ensure patients have accessible information to understand their condition, assessing the standard of online resources is essential. To assess the quality, content, accountability, and readability of online information for optic neuritis. This cross-sectional study analyzed 11 freely available medical sites with information on optic neuritis and used PubMed as a gold standard for comparison. Twelve questions were composed to include the information most relevant to patients, and each website was independently examined by four neuro-ophthalmologists. Readability was analyzed using an online readability tool. Journal of the American Medical Association (JAMA) benchmarks, four criteria designed to assess the quality of health information further were used to evaluate the accountability of each website. Freely available online information. On average, websites scored 27.98 (SD ± 9.93, 95% CI 24.96–31.00) of 48 potential points (58.3%) for the twelve questions. There were significant differences in the comprehensiveness and accuracy of content across websites (p <.001). The mean reading grade level of websites was 11.90 (SD ± 2.52, 95% CI 8.83–15.25). Zero websites achieved all four JAMA benchmarks. Interobserver reliability was robust between three of four neuro-ophthalmologist (NO) reviewers (ρ = 0.77 between NO3 and NO2, ρ = 0.91 between NO3 and NO1, ρ = 0.74 between NO2 and NO1; all p <.05). The quality of freely available online information detailing optic neuritis varies by source, with significant room for improvement. The material presented is difficult to interpret and exceeds the recommended reading level for health information. Most websites reviewed did not provide comprehensive information regarding non-therapeutic aspects of the disease. Ophthalmology organizations should be encouraged to create content that is more accessible to the general public. [ABSTRACT FROM AUTHOR]

Background: While large language models (LLMs) are increasingly used in medicine, their effectiveness compared with human experts remains unclear. This study evaluates the quality and empathy of Expert + AI, human experts, and LLM responses in neuro-ophthalmology., Methods: This randomized, masked, multicenter cross-sectional study was conducted from June to July 2023. We randomly assigned 21 neuro-ophthalmology questions to 13 experts. Each expert provided an answer and then edited a ChatGPT-4-generated response, timing both tasks. In addition, 5 LLMs (ChatGPT-3.5, ChatGPT-4, Claude 2, Bing, Bard) generated responses. Anonymized and randomized responses from Expert + AI, human experts, and LLMs were evaluated by the remaining 12 experts. The main outcome was the mean score for quality and empathy, rated on a 1-5 scale., Results: Significant differences existed between response types for both quality and empathy (P < 0.0001, P < 0.0001). For quality, Expert + AI (4.16 ± 0.81) performed the best, followed by GPT-4 (4.04 ± 0.92), GPT-3.5 (3.99 ± 0.87), Claude (3.6 ± 1.09), Expert (3.56 ± 1.01), Bard (3.5 ± 1.15), and Bing (3.04 ± 1.12). For empathy, Expert + AI (3.63 ± 0.87) had the highest score, followed by GPT-4 (3.6 ± 0.88), Bard (3.54 ± 0.89), GPT-3.5 (3.5 ± 0.83), Bing (3.27 ± 1.03), Expert (3.26 ± 1.08), and Claude (3.11 ± 0.78). For quality (P < 0.0001) and empathy (P = 0.002), Expert + AI performed better than Expert. Time taken for expert-created and expert-edited LLM responses was similar (P = 0.75)., Conclusions: Expert-edited LLM responses had the highest expert-determined ratings of quality and empathy warranting further exploration of their potential benefits in clinical settings., Competing Interests: The authors report no conflicts of interest., (Copyright © 2024 North American Neuro-Ophthalmology Society.)