{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,27]],"date-time":"2026-08-27T14:18:48Z","timestamp":1787840328376,"version":"build-2784847793"},"reference-count":26,"publisher":"Ovid Technologies (Wolters Kluwer Health)","issue":"6","license":[{"start":{"date-parts":[[2025,8,11]],"date-time":"2025-08-11T00:00:00Z","timestamp":1754870400000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-sa\/4.0"}],"content-domain":{"domain":["lww.com","ovid.com"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2025,11]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Psoriasis is a chronic inflammatory skin condition affecting millions of people globally, with prevalence varying significantly between countries. Conventional treatments, including topical agents, phototherapy, and systemic medications, often fail to account for individual variability, leading to suboptimal outcomes and potential adverse effects. Artificial intelligence (AI) has emerged as a promising approach to enhance precision and personalization in psoriasis management, potentially transforming diagnostic accuracy and treatment selection. This review examines the integration of AI across multiple domains of psoriasis treatment: (1) machine learning algorithms for phototherapy outcome prediction, (2) deep learning techniques for lesion segmentation and severity assessment, (3) AI-enhanced remote photographic monitoring systems, and (4) predictive modeling for response to systemic therapies and biologics. The analysis encompasses various AI methodologies, including random forest classifiers, convolutional neural networks, multiscale superpixel clustering, and gradient-boosted decision trees applied to clinical datasets, imaging analysis, and multi-omic patient data. AI-driven models demonstrate significant clinical utility with phototherapy outcome prediction, achieving high sensitivity (&gt;84%) and accuracy (75-85%). Automated lesion segmentation reaches 86.99%-pixel accuracy, while remote AI assessments strongly correlate with clinical evaluations (Intraclass Correlation Coefficient [ICC] = 0.78-0.99). Notably, predictive models can forecast biologic therapy responses with &gt; 95% accuracy within 2-4 weeks of treatment initiation, substantially reducing evaluation timelines from the conventional 12-week assessment period. AI technologies offer transformative potential in psoriasis management by enabling precise diagnosis, outcome prediction, and personalized therapy selection. Current implementations show promising results across diverse clinical applications, from phototherapy optimization to biologic response prediction. While challenges in dataset diversity, standardization, and validation remain, these represent opportunities for further advancement toward precision medicine in dermatology.<\/jats:p>","DOI":"10.4103\/idoj.idoj_1055_24","type":"journal-article","created":{"date-parts":[[2025,8,11]],"date-time":"2025-08-11T14:00:10Z","timestamp":1754920810000},"page":"894-901","update-policy":"https:\/\/doi.org\/10.1097\/lww.0000000000001000","source":"Crossref","is-referenced-by-count":6,"title":["Insights into the Impact of Artificial Intelligence on Psoriasis Treatment Strategies: A Mini Review"],"prefix":"10.4103","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0009-0000-3129-7327","authenticated-orcid":false,"given":"A","family":"Prithiviraj","sequence":"first","affiliation":[{"name":"Department of Pharmacy Practice, J.K.K. Nattraja College of Pharmacy, Komarapalayam, Namakkal District, Tamil Nadu, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-4322-6183","authenticated-orcid":false,"given":"M A","family":"Aarthi","sequence":"additional","affiliation":[{"name":"Department of Pharmacy Practice, J.K.K. Nattraja College of Pharmacy, Komarapalayam, Namakkal District, Tamil Nadu, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8623-7898","authenticated-orcid":false,"given":"N","family":"Venkateswaramurthy","sequence":"additional","affiliation":[{"name":"Department of Pharmacy Practice, J.K.K. Nattraja College of Pharmacy, Komarapalayam, Namakkal District, Tamil Nadu, India"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"276","published-online":{"date-parts":[[2025,8,11]]},"reference":[{"key":"R1-20251030","doi-asserted-by":"crossref","first-page":"1475","DOI":"10.3390\/ijms20061475","article-title":"Psoriasis Pathogenesis and Treatment","volume":"20","author":"Rendon","year":"2019","journal-title":"Int J Mol Sci"},{"key":"R3-20251030","first-page":"105","article-title":"Combining biologic and phototherapy treatments for psoriasis:safety, efficacy, and patient acceptability","volume":"6","author":"Farahnik","year":"2016","journal-title":"Psoriasis (Auckl)"},{"key":"R4-20251030","doi-asserted-by":"crossref","first-page":"9850","DOI":"10.3390\/ijms24129850","article-title":"Genetic Influence on Treatment Response in Psoriasis:New Insights into Personalized Medicine","volume":"24","author":"Berna-Rico","year":"2023","journal-title":"Int J Mol Sci"},{"key":"R5-20251030","doi-asserted-by":"crossref","first-page":"743","DOI":"10.3390\/ijerph13070743","article-title":"Environmental Risk Factors in Psoriasis:The Point of View of the Nutritionist","volume":"13","author":"Barrea","year":"2016","journal-title":"Int J Environ Res Public Health"},{"key":"R6-20251030","first-page":"2","article-title":"A systematic review of adverse effects associated with topical treatments for psoriasis","volume":"9","author":"Bruner","year":"2003","journal-title":"Dermatol Online J"},{"key":"R7-20251030","first-page":"e19182","article-title":"An Update on Narrowband Ultraviolet B Therapy for the Treatment of Skin Diseases","volume":"13","author":"Myers","year":"2021","journal-title":"Cureus"},{"key":"R8-20251030","doi-asserted-by":"crossref","first-page":"1148","DOI":"10.1111\/bjd.16716","article-title":"Narrowband ultraviolet B treatment for psoriasis is highly economical and causes significant savings in cost for topical treatments","volume":"179","author":"Boswell","year":"2018","journal-title":"Br J Dermatol"},{"key":"R9-20251030","first-page":"31","article-title":"A Practical Approach to Home UVB Phototherapy for the Treatment of Generalized Psoriasis","volume":"7","author":"Bhutani","year":"2010","journal-title":"Pract Dermatol"},{"issue":"Suppl 3","key":"R10-20251030","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1111\/j.1468-3083.2012.04518.x","article-title":"Evidence-based recommendations on topical treatment and phototherapy of psoriasis:systematic review and expert opinion of a panel of dermatologists","volume":"26","author":"Paul","year":"2012","journal-title":"J Eur Acad Dermatol Venereol"},{"key":"R11-20251030","doi-asserted-by":"crossref","first-page":"253","DOI":"10.2147\/CCID.S296604","article-title":"A Priori Estimation of the Narrow-Band UVB Phototherapy Outcome for Moderate-to-Severe Psoriasis Based on the Patients'Questionnaire and Blood Tests Using Random Forest Classifier","volume":"14","author":"Narbutt","year":"2021","journal-title":"Clin Cosmet Investig Dermatol"},{"key":"R12-20251030","doi-asserted-by":"crossref","first-page":"226","DOI":"10.1016\/j.bspc.2019.04.002","article-title":"PsLSNet:Automated psoriasis skin lesion segmentation using modified U-Net-based fully convolutional network","volume":"52","author":"Dash","year":"2019","journal-title":"Biomed. 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