ReviewAmerican journal of clinical dermatology2024
Image-Based Artificial Intelligence in Psoriasis Assessment: The Beginning of a New Diagnostic Era?
Review in American journal of clinical dermatology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
19 citing papers in PubMed.
- Exploring the role of neuro-immune modulation for psoriasis management: a narrative review of existing evidence and future therapies.Brain, behavior, & immunity - health · 2026Review
- Cell-based therapies of autoimmune diseases in the context of artificial intelligence development.Clinical and experimental medicine · 2026Review
- Artificial intelligence-based quantification of epidermal proliferation and apoptosis in human skin.JID innovations : skin science from molecules to population health · 2026Article
- Sex representation in trials relative to indication-specific disease burden in FDA-approved drugs (2015-2023).Nature communications · 2026Article
- Artificial Intelligence in Dermatology: New Regulatory Aspects After the European AI Act.Skin research and technology : official journal of International Society for Bioengineering and the Skin (ISBS) [and] International Society for Digital Imaging of Skin (ISDIS) [and] International Society for Skin Imaging (ISSI) · 2026Article
- Synthetic skin image generation using a physics-based, object-to-image computational pipeline.International journal of computer assisted radiology and surgery · 2026Article
- Forensic 3D avatars - AI-assisted injury detection and interactive digital-twin visualization.Forensic science, medicine, and pathology · 2026Article
- Review
- Frequency of cardiovascular diseases in patients of psoriasis presenting at a tertiary care hospital in Karachi Pakistan.Pakistan journal of medical sciences · 2026Article
- Intelligent Diagnosis and Treatment of Psoriasis Research: A Bibliometric Analysis from 2005-01-01 to 2025-12-31.Clinical, cosmetic and investigational dermatology · 2026Article
- From static pathology to dynamic immunity: immunological plasticity and histopathological remodeling in atopic dermatitis and psoriasis.Frontiers in immunology · 2026Review
- Artificial intelligence-based automated grading and severity prediction of radiation dermatitis: a review.Radiation oncology (London, England) · 2025Review
- Insights into the Impact of Artificial Intelligence on Psoriasis Treatment Strategies: A Mini Review.Indian dermatology online journal · 2025Article
- Rethinking arthritis: exploring its types and emerging management strategies.Inflammopharmacology · 2025Review
- Dermatoscopic Patterns in Mycosis Fungoides: Observations from a Case-Series Retrospective Analysis and a Review of the Literature.Diagnostics (Basel, Switzerland) · 2025Article
- Precision management in chronic disease: An AI empowered perspective on medicine-engineering crossover.iScience · 2025Review
- Digital technologies in psoriasis management: from precision diagnosis to therapeutic innovation and holistic care.Frontiers in digital health · 2025Review
- AI in Aesthetic/Cosmetic Dermatology: Current and Future.Journal of cosmetic dermatology · 2025Review
- Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Psoriasis, a chronic inflammatory skin disease, affects millions of people worldwide. It imposes a significant burden on patients' quality of life and healthcare systems, creating an urgent need for optimized diagnosis, treatment, and management. In recent years, image-based artificial intelligence (AI) applications have emerged as promising tools to assist physicians by offering improved accuracy and efficiency. In this review, we provide an overview of the current landscape of image-based AI applications in psoriasis. Emphasis is placed on machine learning (ML) algorithms, a key subset of AI, which enable automated pattern recognition for various tasks. Key AI applications in psoriasis include lesion detection and segmentation, differentiation from other skin conditions, subtype identification, automated area involvement, and severity scoring, as well as personalized treatment selection and response prediction. Furthermore, we discuss two commercially available systems that utilize standardized photo documentation, automated segmentation, and semi-automated Psoriasis Area and Severity Index (PASI) calculation for patient assessment and follow-up. Despite the promise of AI in this field, many challenges remain. These include the validation of current models, integration into clinical workflows, the current lack of diversity in training-set data, and the need for standardized imaging protocols. Addressing these issues is crucial for the successful implementation of AI technologies in clinical practice. Overall, we underscore the potential of AI to revolutionize psoriasis management, highlighting both the advancements and the hurdles that need to be overcome. As technology continues to evolve, AI is expected to significantly improve the accuracy, efficiency, and personalization of psoriasis treatment.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.