Reference health
AI in Psoriatic Disease: Scoping Review
34/34 checkable references clean · checked 2026-08-25
Every reference with a DOI in the deposited reference list resolved to a known work in Crossref or DataCite at the dated check, and none carried a retraction, withdrawal, or removal notice.
6 without a DOI — not checked. A reference deposited without a DOI is never matched by title or guessed at; it stays outside the checked set, and this line discloses that.
The 34 checked references that resolve
resolves10.3389/fmed.2020.00100
Artificial Intelligence Applications in Dermatology: Where Do We Stand?
resolves10.1007/s40257-019-00462-6
Artificial Intelligence in Dermatology—Where We Are and the Way to the Future: A Review
resolves10.1001/jamadermatol.2013.5015
Psoriasis Severity and the Prevalence of Major Medical Comorbidity
resolves10.1016/j.jaad.2011.11.948
Risks of developing psychiatric disorders in pediatric patients with psoriasis
resolves10.1111/bjd.20482
Patient preferences for stratified medicine in psoriasis: a discrete choice experiment*
resolves10.1016/j.jaci.2023.07.011
Integrated proteomics and genomics analysis of paradoxical eczema in psoriasis patients treated with biologics
resolves10.1364/BOE.10.000879
Smartphone-based multispectral imaging and machine-learning based analysis for discrimination between seborrheic dermatitis and psoriasis on the scalp
resolves10.1111/jdv.15965
Smart identification of psoriasis by images using convolutional neural networks: a case study in China
resolves10.1016/j.cmpb.2015.11.013
Computer-aided diagnosis of psoriasis skin images with HOS, texture and color features: A first comparative study of its kind
resolves10.2196/44932
Artificial Intelligence–Based Psoriasis Severity Assessment: Real-world Study and Application
resolves10.1111/jdv.18354
Artificial intelligence for the automated single‐shot assessment of psoriasis severity
resolves10.1111/bjd.19039
British Association of Dermatologists guidelines for biologic therapy for psoriasis 2020: a rapid update
resolves10.1111/bjd.18741
Predicting the long‐term outcomes of biologics in patients with psoriasis using machine learning
resolves10.1001/jamadermatol.2022.3171
Multivariable Predictive Models to Identify the Optimal Biologic Therapy for Treatment of Patients With Psoriasis at the Individual Level
resolves10.3389/fimmu.2022.847312
Harnessing Big Data, Smart and Digital Technologies and Artificial Intelligence for Preventing, Early Intercepting, Managing, and Treating Psoriatic Arthritis: Insights From a Systematic Review of the Literature
resolves10.1038/s41467-018-06672-6
Genetic signature to provide robust risk assessment of psoriatic arthritis development in psoriasis patients
resolves10.1016/j.semarthrit.2010.05.002
Validation of Psoriatic Arthritis Diagnoses in Electronic Medical Records Using Natural Language Processing
resolves10.1093/bmb/ldab016
The promise of artificial intelligence: a review of the opportunities and challenges of artificial intelligence in healthcare
resolves10.1002/jvc2.93
Telemedicine and psoriasis: A review based on statements of the telemedicine working group of the International Psoriasis Council
resolves10.1111/bjd.20663
Can artificial intelligence be used for accurate remote scoring of the Psoriasis Area and Severity Index in adult patients with plaque psoriasis?
resolves10.1007/s13555-020-00372-0
Machine Learning in Dermatology: Current Applications, Opportunities, and Limitations
resolves10.1016/j.jaad.2021.06.884
Bias in, bias out: Underreporting and underrepresentation of diverse skin types in machine learning research for skin cancer detection—A scoping review
resolves10.2196/37611
The Adoption of Artificial Intelligence in Health Care and Social Services in Australia: Findings From a Methodologically Innovative National Survey of Values and Attitudes (the AVA-AI Study)
resolves10.1111/bjd.18875
Attitudes towards artificial intelligence within dermatology: an international online survey
resolves10.2196/36823
Guidelines for Artificial Intelligence in Medicine: Literature Review and Content Analysis of Frameworks
The 6 references without a DOI — listed, not checked
no DOI — not checkedArtificial intelligence (AI) at HHSU.S. Department of Health and Human Services2024-07-26https://www.hhs.gov/about/agencies/asa/ocio/ai/index.html
no DOI — not checkedArtificial intelligence in healthcare: applications, risks, and ethical and societal impactsEuropean Parliament20222024-07-26https://www.europarl.europa.eu/thinktank/en/document/EPRS_STU(2022)729512
no DOI — not checkedThe Topol Review2024-07-26https://topol.hee.nhs.uk/the-topol-review/
no DOI — not checkedLandiHGoogle debuts AI-powered app to help consumers identify common skin conditionsFierce Healthcare20212023-09-10https://www.fiercehealthcare.com/tech/google-previews-ai-dermatology-tool-to-help-consumers-identify-skin-conditions
no DOI — not checkedScoviakMAI diagnostics fall short in skin of colorDermatology Times20212023-09-10https://www.dermatologytimes.com/view/ai-diagnostics-fall-short-in-skin-of-color
no DOI — not checkedEuropean approach to artificial intelligenceShaping Europe's Digital Future2023-10-22https://digital-strategy.ec.europa.eu/en/policies/european-approach-artificial-intelligence
What this badge says. CiteStamped means the CHECKABLE references of this
work were clean at the dated check: each resolved to a known work in a public registry, and none
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