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Cited by 65Psoriasis: Treatment and PathogenesisArtificial Intelligence in Healthcare and EducationArtificial Intelligence in Healthcare

Machine learning applications in the evaluation and management of psoriasis: a systematic review

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Yu, Kimberley, Syed, Maha N., Bernardis, Elena, Gelfand, Joel M.·2020·Journal of Psoriasis and Psoriatic Arthritis
DOI 10.1177/2475530320950267PMC PMC7963214
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Abstract

Background: Machine learning (ML), a subset of artificial intelligence (AI) that aims to teach machines to automatically learn tasks by inferring patterns from data, holds significant promise to aid psoriasis care. Applications include evaluation of skin images for screening and diagnosis as well as clinical management including treatment and complication prediction. Objective: To summarize literature on ML applications to psoriasis evaluation and management and to discuss challenges and opportunities for future advances. Methods: We searched MEDLINE, Google Scholar, ACM Digital Library, and IEEE Xplore for peer-reviewed publications published in English through December 1, 2019. Our search queries identified publications with any of the 10 computing-related keywords and “psoriasis” in the title and/or abstract. Results: Thirty-three studies were identified. Articles were organized by topic and synthesized as evaluation- or management-focused articles covering 5 content categories: (A) Evaluation using skin images: (1) identification and differential diagnosis of psoriasis lesions, (2) lesion segmentation, and (3) lesion severity and area scoring; (B) clinical management: (1) prediction of complications and (2) treatment. Conclusion: Machine learning has significant potential to aid psoriasis evaluation and management. Current topics popular in ML research on psoriasis are the evaluation of medical images, prediction of complications, and treatment discovery. For patients to derive the greatest benefit from ML advancements, it is helpful for dermatologists to have an understanding of ML and how it can effectively aid their assessments and decision-making.

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  • A meta-analysis evaluating the performance of image-based AI models for skin disease severity assessment has not been reported.

    “Notably, a meta-analysis evaluating the performance of image-based AI models for skin disease severity assessment has not been reported.15,16”

    From Assessing the performance of artificial intelligence models in evaluating inflammatory skin disease severity: a systematic review and meta-analysis (2025) · cites this paper as [15]

    The Results (page unavailable) say this review qualitatively synthesized 33 studies, including 12 on image-based lesion severity and area scoring; the Methods likewise describe a qualitative synthesis. That supports the narrower point that this review did not perform a performance meta-analysis, but the paper does not establish the broader claim that no such meta-analysis had been reported elsewhere. (LLM, conf=0.78)

    Experimental — accuracy not yet measured

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Cited by

Sorted by citations

Authors
Yu, K., Syed, M. N., Bernardis, E., & Gelfand, J. M.
Affiliations
University of Pennsylvania (US)
Corresponding author
Yu, Kimberley
ORCID
Venue
Journal of Psoriasis and Psoriatic Arthritis
Publisher
SAGE Publications
Published
2020-08-31
Volume
5
Issue
4
Pages
147-159
ISSN
  • 2475-5303 (ISSN-L)
  • 2475-5311
DOI
10.1177/2475530320950267
PMC
PMC7963214
OpenAlex
W3082120157
Cited by
65
References
6
Citations by year
2026: 7 · 2025: 16 · 2024: 16 · 2023: 14 · 2022: 7 · 2021: 5
FWCI
0.63
Type
journal article
Language
en
Open access
Licence
journals.sagepub.com/page/policies/text-and-data-mining-license (tdm)
Locations
  • Journal of Psoriasis and Psoriatic Arthritis (publishedVersion)
  • PubMed (publishedVersion)
  • PubMed Central (acceptedVersion)
Funders
University of Pennsylvania (Center for Clinical Epidemiology and Biostatistics)
Topics
  • Life Sciences › Immunology and Microbiology › Immunology › Psoriasis: Treatment and Pathogenesis
  • Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
  • Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
Keywords
psoriasis, machine learning, artificial intelligence, skin lesion segmentation, psoriasis diagnosis, differential diagnosis, treatment prediction, complication prediction, dermatology

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