{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,30]],"date-time":"2026-09-30T18:34:24Z","timestamp":1790793264789,"version":"4.1.0"},"reference-count":18,"publisher":"Wiley","issue":"3","license":[{"start":{"date-parts":[[2025,11,6]],"date-time":"2025-11-06T00:00:00Z","timestamp":1762387200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"},{"start":{"date-parts":[[2025,11,6]],"date-time":"2025-11-06T00:00:00Z","timestamp":1762387200000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Int J Dermatology"],"published-print":{"date-parts":[[2026,3]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:p>\n                    Artificial intelligence (AI) techniques can allow for early diagnosis and treatment of acne. Bias in AI model training remains, leading to various challenges in achieving health equity in clinical practice. We aim to assess and provide an updated overview of (1) the types of AI\u2010based tools developed for acne, (2) the various applications of AI in acne diagnosis and management, (3) the performance of these tools, and (4) the current data reported on skin diversity in AI model training. [Correction added on 27 December 2025, after first online publication: The preceding sentence has been corrected.] We queried PubMed, Cochrane and Scopus databases using the terms: \u201cacne\u201d, \u201cartificial intelligence\u201d, \u201cmachine learning\u201d, \u201cdeep learning\u201d, \u201clarge language model\u201d, and \u201cChatGPT\u201d. 105 articles were included for analysis. Of the 105 research articles, 96.2% (\n                    <jats:italic>N<\/jats:italic>\n                    \u2009=\u2009101) were focused on acne diagnosis only, 9.5% (\n                    <jats:italic>N<\/jats:italic>\n                    \u2009=\u200910) on acne management only, and 5.7% (\n                    <jats:italic>N<\/jats:italic>\n                    \u2009=\u20096) on both. Most manuscripts used image\u2010based models, including deep learning (76.2%,\n                    <jats:italic>N<\/jats:italic>\n                    \u2009=\u200980), classical machine learning (9.5%,\n                    <jats:italic>N<\/jats:italic>\n                    \u2009=\u200910), and ensemble models (11.4%,\n                    <jats:italic>N<\/jats:italic>\n                    \u2009=\u200912). The ensemble models hold the highest mean accuracy (89.7%), followed by deep learning (88.5%), large language models (87.5%), and machine learning models (86.9%). Only 13% (\n                    <jats:italic>N<\/jats:italic>\n                    \u2009=\u200914) of studies reported data on patient skin color, while 4 of the 14 studies included a full spectrum of diverse skin tones. [Correction added on 27 December 2025, after first online publication: The preceding sentence has been corrected.] The application of\n                    <jats:styled-content style=\"fixed-case\">AI<\/jats:styled-content>\n                    algorithms in healthcare is rapidly emerging, providing significant support to providers. With ensemble models demonstrating superior performance,\n                    <jats:styled-content style=\"fixed-case\">AI<\/jats:styled-content>\n                    algorithm use in acne may offer a convenient method to consistently diagnose and manage patients remotely. Designing systematic guidelines that require a diverse representation of all skin colors may improve social justice in healthcare.\n                  <\/jats:p>","DOI":"10.1111\/ijd.70110","type":"journal-article","created":{"date-parts":[[2025,11,7]],"date-time":"2025-11-07T07:15:38Z","timestamp":1762499738000},"page":"437-443","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Artificial Intelligence Use in Acne Diagnosis and Management\u2014A Scoping Review"],"prefix":"10.1111","volume":"65","author":[{"ORCID":"https:\/\/orcid.org\/0009-0007-9318-6535","authenticated-orcid":false,"given":"Katie L.","family":"Frederickson","sequence":"first","affiliation":[{"name":"Meharry Medical College  Nashville Tennessee USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0564-940X","authenticated-orcid":false,"given":"Haiwen","family":"Gui","sequence":"additional","affiliation":[{"name":"Department of Dermatology Stanford University  Redwood City California USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5467-4102","authenticated-orcid":false,"given":"John S.","family":"Barbieri","sequence":"additional","affiliation":[{"name":"Department of Dermatology Brigham and Women's Hospital  Boston Massachusetts USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7988-9356","authenticated-orcid":false,"given":"Roxana","family":"Daneshjou","sequence":"additional","affiliation":[{"name":"Department of Dermatology Stanford University  Redwood City California USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2025,11,6]]},"reference":[{"issue":"4","key":"e_1_2_8_2_1","first-page":"577","article-title":"Acne Vulgaris Detection and Classification: A Dual Integrated Deep CNN Model","volume":"47","author":"Islam M. B.","year":"2023","journal-title":"Lithuanian Academy of Sciences. Informatica"},{"key":"e_1_2_8_3_1","doi-asserted-by":"publisher","DOI":"10.3389\/fmed.2024.1344314"},{"issue":"9","key":"e_1_2_8_4_1","first-page":"1006","article-title":"Artificial Intelligence for the Objective Evaluation of Acne Investigator Global Assessment","volume":"17","author":"Melina A.","year":"2018","journal-title":"Journal of Drugs in Dermatology"},{"key":"e_1_2_8_5_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jaad.2023.12.017"},{"key":"e_1_2_8_6_1","doi-asserted-by":"publisher","DOI":"10.1001\/jamadermatol.2018.3022"},{"key":"e_1_2_8_7_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jid.2024.05.030"},{"key":"e_1_2_8_8_1","doi-asserted-by":"publisher","DOI":"10.1177\/20552076231205740"},{"key":"e_1_2_8_9_1","doi-asserted-by":"publisher","DOI":"10.1001\/jama.2023.2771"},{"key":"e_1_2_8_10_1","doi-asserted-by":"publisher","DOI":"10.3389\/fmed.2023.1278232"},{"key":"e_1_2_8_11_1","doi-asserted-by":"publisher","DOI":"10.1038\/nature14539"},{"key":"e_1_2_8_12_1","unstructured":"W.Li Y.Peng M.Zhang L.Ding H.Hu andL.Shen \u201cDeep Model Fusion: A Survey 2013\u201darXiv [Cs.LG]. arXiv."},{"key":"e_1_2_8_13_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.heliyon.2024.e31965"},{"key":"e_1_2_8_14_1","doi-asserted-by":"publisher","DOI":"10.1111\/ijd.14071"},{"key":"e_1_2_8_15_1","doi-asserted-by":"publisher","DOI":"10.1111\/srt.13693"},{"key":"e_1_2_8_16_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jid.2018.03.586"},{"key":"e_1_2_8_17_1","doi-asserted-by":"publisher","DOI":"10.3389\/fmed.2020.574329"},{"key":"e_1_2_8_18_1","doi-asserted-by":"publisher","DOI":"10.1001\/jamadermatol.2021.3129"},{"key":"e_1_2_8_19_1","doi-asserted-by":"publisher","DOI":"10.1109\/ElConRus51938.2021.9396382"}],"container-title":["International Journal of Dermatology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1111\/ijd.70110","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/full-xml\/10.1111\/ijd.70110","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1111\/ijd.70110","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,8]],"date-time":"2026-03-08T09:51:12Z","timestamp":1772963472000},"score":1,"resource":{"primary":{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/10.1111\/ijd.70110"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,11,6]]},"references-count":18,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2026,3]]}},"alternative-id":["10.1111\/ijd.70110"],"URL":"https:\/\/doi.org\/10.1111\/ijd.70110","archive":["Portico"],"relation":{},"ISSN":["0011-9059","1365-4632"],"issn-type":[{"value":"0011-9059","type":"print"},{"value":"1365-4632","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,11,6]]},"assertion":[{"value":"2025-07-16","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2025-10-09","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2025-11-06","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}