Background Large language models such as ChatGPT are increasingly used by patients seeking perioperative information, yet their reliability for anesthesia-related patient education remains insufficiently evaluated. This study assessed the quality of ChatGPT-4.0 responses to frequently asked anesthesia questions using a multi-rater evaluation framework. Methods Twenty-two common anesthesia-related patient questions were identified through online search. Each question was submitted once to ChatGPT-4.0 (GPT-4-turbo; chat.openai.com) without follow-up prompts. Five anesthesiology and reanimation specialists—each with more than 20 years of experience—independently evaluated each response using a validated 4-point Likert-type scale (1 = excellent; 4 = unsatisfactory). Inter-rater reliability was calculated using a two-way random-effects model (ICC2,1). Results A total of 110 ratings were collected. Among these, 61.8% were classified as excellent, 32.7% as satisfactory requiring minimal clarification, and 5.5% as satisfactory requiring moderate clarification. No responses were rated as unsatisfactory. Mean scores for individual questions ranged from 1.0 to 2.4. Reviewer-wise averages ranged from 1.27 to 1.73, indicating generally positive evaluations with modest variability in scoring strictness. The overall inter-rater reliability was poor to fair (ICC = 0.25). Conclusions ChatGPT-4.0 provided high-quality responses to frequently asked patient questions about anesthesia and may serve as a supportive digital health tool for patient education. However, limited agreement among evaluators highlights the need for expert oversight and contextual refinement when integrating large language models into clinical communication pathways.
Akçaalan et al. (Sun,) studied this question.