{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,9]],"date-time":"2026-09-09T06:07:42Z","timestamp":1788934062056,"version":"build-2803163510"},"reference-count":21,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,9,9]],"date-time":"2026-09-09T00:00:00Z","timestamp":1788912000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Psychiatry"],"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Due to structural and socioeconomic barriers to accessing the healthcare system, individuals are increasingly turning to online platforms to obtain health-related information. With the advancement of artificial intelligence (AI), the use of AI for this purpose is also on the rise. It can also be said that seeking information from AI regarding psychotherapies is increasingly common.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>This study aimed to assess the informational quality and readability of AI-generated psychoeducational content about psychotherapy. To this end, we evaluated the informational quality and readability of LLM-generated Eye Movement Desensitization and Reprocessing (EMDR) psychoeducational content across different models.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>In this cross-sectional content analysis, EMDR-related search queries were identified through Google Trends, converted into patient-facing questions, and subsequently organized into four thematic categories, with five questions selected from each category for evaluation using ChatGPT (GPT-4o), Gemini (1.5 Pro), and DeepSeek (V3). Each question was entered in three separate sessions, yielding 180 responses. The informational quality was assessed by two blinded psychiatrists using the Ensuring Quality Information for Patients (EQIP) instrument. Readability was evaluated using the Flesch\u2013Kincaid Grade Level (FKGL) and Flesch Reading Ease (FRE) indices. Statistical analyses included linear mixed-effects models and ANOVA.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>The overall mean EQIP score was 56.08 (SD\u00a0=\u00a04.43). Informational quality differed significantly across the models (p &amp;lt; 0.001), and no significant differences were observed across EMDR-related question categories. The highest informational quality scores were obtained by Gemini, followed by DeepSeek and ChatGPT. DeepSeek generated the most readable responses. Higher EQIP scores were seen to be associated with greater readability and longer response length. All the evaluated models produced responses that exceeded the commonly recommended readability levels for patient education materials. Overall inter-rater consistency was high for the total EQIP scores.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>Although all the LLMs evaluated were able to generate psychoeducational information about EMDR, substantial differences were observed between the models in respect of informational quality and readability. The findings suggest that psychoeducational quality depends more strongly on the language model used than on the specific EMDR-related topic being addressed. LLMs may serve as supplementary psychoeducational resources but should not be considered as substitutes for professional psychotherapy or individualized clinical care.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fpsyt.2026.1937506","type":"journal-article","created":{"date-parts":[[2026,9,9]],"date-time":"2026-09-09T05:58:55Z","timestamp":1788933535000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Artificial intelligence as a source of psychotherapy-related psychoeducation: a cross-sectional content analysis of information on eye movement desensitization and reprocessing"],"prefix":"10.3389","volume":"17","author":[{"given":"\u0130brahim","family":"Karakaya","sequence":"first","affiliation":[{"name":"Private Psychiatric Practice","place":["Istanbul, T\u00fcrkiye"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Onur Okan","family":"Demirci","sequence":"additional","affiliation":[{"name":"Private Psychiatric Practice","place":["Istanbul, T\u00fcrkiye"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2026,9,9]]},"reference":[{"key":"B1","volume-title":"Eye Movement Desensitization and Reprocessing (Emdr) Therapy: Basic Principles, Protocols, and Procedures","author":"Shapiro","year":"2017"},{"key":"B2","doi-asserted-by":"publisher","DOI":"10.1017\/S0033291717000708","article-title":"Posttraumatic stress disorder in the World Mental Health Surveys","volume":"47","author":"Koenen","year":"2017","journal-title":"Psychol Med"},{"key":"B3","doi-asserted-by":"publisher","DOI":"10.1001\/jamainternmed.2023.1838","article-title":"Comparing physician and artificial intelligence chatbot responses to patient questions trusted in public social media forums","volume":"183","author":"Ayers","year":"2023","journal-title":"JAMA Intern 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for mental health practice","volume":"25","author":"Maurya","year":"2025","journal-title":"Counselling Psychother Res"},{"key":"B15","doi-asserted-by":"publisher","DOI":"10.2196\/77951","article-title":"Seeking emotional and mental health support from generative AI: Mixed-methods study of ChatGPT user experiences","volume":"12","author":"Luo","year":"2025","journal-title":"JMIR Ment Health"},{"key":"B16","doi-asserted-by":"publisher","first-page":"17","DOI":"10.59249\/ZTOZ1966","article-title":"Assessing the efficacy of large language models in health literacy: A comprehensive cross-sectional study","volume":"97","author":"Amin","year":"2024","journal-title":"Yale J Biol Med"},{"key":"B17","doi-asserted-by":"publisher","DOI":"10.47482\/acmr.1893217","article-title":"Performance of DeepSeek vs. established large language models in answering frequently asked questions about refractive surgery","volume":"7","author":"Aribas","year":"2026","journal-title":"Acad Clin Med 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