A Comparative Study of Large Language Models, Human Experts, and Expert-Edited Large Language Models to Neuro-Ophthalmology Questions

移情 心理学 质量(理念) 医学 精神科 认识论 哲学
作者
Prashant D. Tailor,Lauren A. Dalvin,Matthew R. Starr,Deena Tajfirouz,Kevin D. Chodnicki,Michael C. Brodsky,Sasha A. Mansukhani,Heather E. Moss,Kevin E. Lai,Melissa W. Ko,Devin D. Mackay,Marie A. Di Nome,Oana M. Dumitrascu,Misha Pless,Eric Eggenberger,John J. Chen
出处
期刊:Journal of Neuro-ophthalmology [Lippincott Williams & Wilkins]
被引量:5
标识
DOI:10.1097/wno.0000000000002145
摘要

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.
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