{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,22]],"date-time":"2026-09-22T19:04:47Z","timestamp":1790103887481,"version":"4.0.1"},"reference-count":0,"publisher":"Oxford University Press (OUP)","license":[{"start":{"date-parts":[[2026,9,22]],"date-time":"2026-09-22T00:00:00Z","timestamp":1790035200000},"content-version":"am","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/pages\/standard-publication-reuse-rights"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"abstract":"Abstract<\/jats:title>\n \n Background<\/jats:title>\n The therapeutic landscape for moderate-to-severe psoriasis has expanded rapidly with biologics and small molecules, increasing the complexity of clinical decision-making. Although large language models (LLMs) may support medical information synthesis, their reliability in high-stakes dermatologic management remains uncertain.<\/jats:p>\n <\/jats:sec>\n \n Objectives<\/jats:title>\n To compare the guideline adherence, safety, and clinical decision quality of multiple LLMs with dermatology experts in systemic psoriasis management.<\/jats:p>\n <\/jats:sec>\n \n Methods<\/jats:title>\n A 40-scenario question bank was developed from the Living EuroGuiDerm Guideline for the Systemic Treatment of Psoriasis Vulgaris and stratified by risk level. Five LLMs (ChatGPT-5.2, Claude Sonnet 4.5, Gemini 3.0 Pro, Grok 4.1 Thinking, and DeepSeek V3.2) were benchmarked against five board-certified dermatologists. Responses were scored for completeness and safety.<\/jats:p>\n <\/jats:sec>\n \n Results<\/jats:title>\n Normalized completeness scores differed significantly among study groups (\u03c72 = 20.55, df = 5, P &lt; 0.001, \u03b52 = 0.066). Gemini 3.0 Pro, DeepSeek V3.2, and Grok 4.1 Thinking scored significantly higher than the expert panel. This advantage was driven primarily by must-know items, for which hit rates differed significantly among groups (P = 0.003; 68% for the human benchmark vs 79\u201390% for LLMs). In low-risk scenarios, AI models outperformed experts (\u03c72 = 17.54, P = 0.004), whereas high-risk performance converged (\u03c72 = 6.14, P = 0.293). However, safety rankings reversed on critical error analysis (Q = 17.10, df = 5, P = 0.004): experts made 1 critical error, while LLMs made 5\u20139 each. Experts made no critical errors in high-risk scenarios, whereas all LLMs made at least one.<\/jats:p>\n <\/jats:sec>\n \n Conclusions<\/jats:title>\n Current LLMs can match or exceed dermatologists in completeness of guideline-based systemic psoriasis recommendations, particularly in low-risk contexts. However, they remain more prone to critical safety errors in complex, high-stakes scenarios.<\/jats:p>\n <\/jats:sec>","DOI":"10.1093\/ced\/llag417","type":"journal-article","created":{"date-parts":[[2026,9,22]],"date-time":"2026-09-22T11:38:19Z","timestamp":1790077099000},"source":"Crossref","is-referenced-by-count":0,"title":["Guideline-Based Evaluation of Large Language Models in Psoriasis Treatment"],"prefix":"10.1093","author":[{"given":"Kuanyu","family":"Xia","sequence":"first","affiliation":[{"name":"Department of Dermatology, Xiangya Hospital, Central South University , Changsha ,","place":["China"]},{"name":"Hunan Key Laboratory of Aging Biology, Xiangya Hospital, Central South University , Changsha ,","place":["China"]},{"name":"National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University , Changsha ,","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-8507-4546","authenticated-orcid":false,"given":"Lang","family":"Min","sequence":"additional","affiliation":[{"name":"Department of Hematology, West China Hospital, Sichuan University , Chengdu, Sichuan ,","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5879-2598","authenticated-orcid":false,"given":"Dan","family":"Jian","sequence":"additional","affiliation":[{"name":"Department of Dermatology, Xiangya Hospital, Central South University , Changsha ,","place":["China"]},{"name":"Hunan Key Laboratory of Aging Biology, Xiangya Hospital, Central South University , Changsha ,","place":["China"]},{"name":"National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University , Changsha ,","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2026,9,22]]},"container-title":["Clinical and Experimental Dermatology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/academic.oup.com\/ced\/advance-article-pdf\/doi\/10.1093\/ced\/llag417\/71319685\/llag417.pdf","content-type":"application\/pdf","content-version":"am","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/ced\/advance-article-pdf\/doi\/10.1093\/ced\/llag417\/71319685\/llag417.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,9,22]],"date-time":"2026-09-22T18:36:03Z","timestamp":1790102163000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/ced\/advance-article\/doi\/10.1093\/ced\/llag417\/8828993"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,9,22]]},"references-count":0,"URL":"https:\/\/doi.org\/10.1093\/ced\/llag417","relation":{},"ISSN":["0307-6938","1365-2230"],"issn-type":[{"value":"0307-6938","type":"print"},{"value":"1365-2230","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,9,22]]},"article-number":"llag417"}}