文献类型：期刊文献

英文题名：Information quality, readability, and empathy of AI-generated public mental health information: a comparative evaluation of eight large language models

作者：Wang, Qilong[1,2];Ma, Siheng[3];Sun, Jian[1];Qi, Xin[2];Zhao, Jianpeng[2];Ma, Dongmei[2];Liu, Xiaofei[4];An, Qiang[5];Zhao, Dongrong[2];He, Songkai[1]

第一作者：Wang, Qilong;王秋兰;王巧丽

通信作者：He, SK[1];Zhao, DR[2]

机构：[1]Xihua Univ, Chengdu, Sichuan, Peoples R China;[2]Gansu Univ Chinese Med, Gansu Prov Peoples Hosp, Clin Coll 1, Dept Psychiat, Lanzhou, Gansu, Peoples R China;[3]Fourth Mil Med Univ, Xijing Hosp, Dept Psychiat, Xian, Shaanxi, Peoples R China;[4]Xian Med Univ, Xian, Shaanxi, Peoples R China;[5]Fourth Mil Med Univ, Dept Biomed Engn, Xian, Shaanxi, Peoples R China

第一机构：Xihua Univ, Chengdu, Sichuan, Peoples R China

通信机构：[1]corresponding author), Xihua Univ, Chengdu, Sichuan, Peoples R China;[2]corresponding author), Gansu Univ Chinese Med, Gansu Prov Peoples Hosp, Clin Coll 1, Dept Psychiat, Lanzhou, Gansu, Peoples R China.|[10735]甘肃中医药大学;

年份：2026

卷号：14

外文期刊名：FRONTIERS IN PUBLIC HEALTH

收录：;Scopus(收录号：2-s2.0-105044503137);WOS:【SSCI(收录号:WOS:001815894000001)，SCI-EXPANDED(收录号:WOS:001815894000001)】；

基金：The author(s) declared that financial support was received for this work and/or its publication. The article processing charge was supported by the Sichuan Key Research Base of Philosophy and Social Sciences, Sichuan Center for Sex Sociology and Sex Education Research (Grant No. SXJYB2534).

语种：英文

外文关键词：empathy; information quality; large language models; patient education; psychiatry; readability

摘要：Background Mental disorders are a growing global health burden, yet healthcare resources remain scarce. Large language models (LLMs) may support public mental health information seeking, but their information quality, readability, and empathy in psychiatric contexts require validation.Method We developed a test bank of 48 public mental health questions from literature, Google Trends (2004-2025), and clinical consultations. Eight LLM chatbots were compared for information quality, source transparency, readability, and empathy using established rating instruments, readability indices, and psychiatrist-rated and user-perspective empathy assessments. Statistical analysis used Kruskal-Wallis and Dunn's post-hoc tests, with Spearman correlations.Results Gemini 3.0 Pro and GPT-5.2 Think showed relatively higher information quality and source transparency scores, whereas spontaneous source transparency was poor across models, with median JAMA scores of 0. No model met the sixth-grade readability standard; Claude Sonnet 4.5 generated relatively more readable responses, whereas Claude Sonnet 4.5 Think produced responses with the highest reading difficulty. In the psychiatrist-rated empathy assessment, Gemini 3.0 Pro and DeepSeek-V3 showed the highest high-empathy response rates, at 54.2% and 43.8%, respectively. User-perspective empathy ratings were generally lower, with DeepSeek-V3 and Gemini 3.0 Pro showing the highest user-perspective high-empathy response rates, at 31.2% and 27.1%, respectively. Information quality and source transparency metrics showed only weak correlations with readability metrics.Conclusion LLMs face important challenges in psychiatric information delivery. Gemini 3.0 Pro and GPT-5.2 Think showed higher information quality and source transparency scores and better information structuring, whereas Claude Sonnet 4.5 generated relatively more readable responses. However, source opacity and high reading difficulty limit direct patient-facing use. Empathy varied across models and differed between psychiatrist-rated and user-perspective assessments, suggesting that empathic communication requires separate optimization and validation with intended users. Future work should balance information quality, source transparency, readability, traceability, safety, and emotionally appropriate responses.