ArticleCurrent dermatology reports2024
Advancing Psoriasis Care through Artificial Intelligence: A Comprehensive Review.
Article in Current dermatology reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 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
8 citing papers in PubMed.
- Advanced hydrogel-based drug delivery systems for psoriasis management: From material design to multi-target therapies.International journal of pharmaceutics: X · 2026Review
- Evaluating the quality of artificial intelligence responses to psoriasis-related clinical and patient questions: a comparative study of ChatGPT, Gemini, and Microsoft Copilot.Proceedings (Baylor University. Medical Center) · 2026Article
- Immunogenetics in psoriasis: towards personalised diagnosis and treatment strategies.Genes and immunity · 2026Review
- Nanocarriers, Smart Biomaterials and Emerging Therapeutics for Psoriasis: Current Progress and Future Directions.AAPS PharmSciTech · 2026Review
- Intelligent Diagnosis and Treatment of Psoriasis Research: A Bibliometric Analysis from 2005-01-01 to 2025-12-31.Clinical, cosmetic and investigational dermatology · 2026Article
- Psoriasis: microbiome dysbiosis and pathogenic mechanisms.Frontiers in immunology · 2026Review
- Dietary patterns and psoriasis severity in Thai patients: a machine learning approach for small sample data.Scientific reports · 2025Article
- Assessing the Impact of ChatGPT in Dermatology: A Comprehensive Rapid Review.Journal of clinical medicine · 2024Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
Abstract
Purpose of Review: Machine learning (ML), a subset of artificial intelligence (AI), has been vital in advancing tasks such as image classification and speech recognition. Its integration into clinical medicine, particularly dermatology, offers a significant leap in healthcare delivery. Recent Findings: This review examines the impact of ML on psoriasis-a condition heavily reliant on visual assessments for diagnosis and treatment. The review highlights five areas where ML is reshaping psoriasis care: diagnosis of psoriasis through clinical and dermoscopic images, skin severity quantification, psoriasis biomarker identification, precision medicine enhancement, and AI-driven education strategies. These advancements promise to improve patient outcomes, especially in regions lacking specialist care. However, the success of AI in dermatology hinges on dermatologists' oversight to ensure that ML's potential is fully realized in patient care, preserving the essential human element in medicine. Summary: This collaboration between AI and human expertise could define the future of dermatological treatments, making personalized care more accessible and precise.
Indexed as
Identifiers
What Socratic holds
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 Socratic graph.