Can Artificial Intelligence Support Patient Education in Scabies? A Comparative Analysis of Large Language Model Responses

Introduction: Artificial intelligence (AI)-based chatbots are becoming an increasingly popular source of health information, particularly for common dermatological conditions such as scabies. However, concerns remain about the accuracy, reliability, quality and readability of the information they provide. Objectives: The aim of this study was to evaluate the accuracy, reliability, quality and readability of responses generated by different AI chatbots in answer to patient questions about scabies. Methods: Scabies-related questions were collected from Quora, a publicly accessible question-and-answer platform, and screened for relevance. Following expert review, 20 representative questions were selected. Responses were generated by three large language models: ChatGPT-5.2, DeepSeek and Claude Sonnet 4.5. The outputs were evaluated by expert reviewers using the hallucination rate, modified DISCERN (mDISCERN), Global Quality Score (GQS), Flesch Reading Ease Score (FRES), and an accuracy assessment based on a 5-point Likert scale. Results: In this study, it was found that ChatGPT-5.2 demonstrated the highest information quality (mDISCERN: 33.6 ± 1.8) and readability (FRES: 63.25 ± 11.5). DeepSeek achieved the highest global quality score (GQS: 5.00 ± 0.00) and accuracy score (5.00 ± 0.00). Claude Sonnet 4.5 had lower scores across most metrics. There were significant differences in hallucination rates among the models (p = 0.003), with DeepSeek exhibiting higher rates. Overall, statistically significant differences were observed among the models in terms of quality, readability and accuracy. Conclusions: AI chatbots provide generally informative but variable-quality responses to scabies-related questions. While DeepSeek demonstrated higher accuracy and overall quality, it also showed higher hallucination rates, whereas ChatGPT-5.2 provided more readable and reliable responses. These findings highlight variability across models and the need for cautious use. AI tools should be considered supportive resources rather than substitutes for professional medical advice.