ReviewJournal of cosmetic dermatology2025
Transforming Skin Quality Evaluation With AI: From Subjective Grading to Data-Driven Precision.
Review in Journal of cosmetic dermatology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 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
4 citing papers in PubMed.
- Oral Postbiotic (VMK223) Supplementation Is Associated with Improved Instrumental Skin Appearance Parameters in Healthy Women Aged 40-55 Years: A Randomized, Double-Blind, Placebo-Controlled Trial.Dermatology and therapy · 2026Article
- Article
- [Artificial intelligence-based holistic, individualized facial analysis in aesthetic medicine].Dermatologie (Heidelberg, Germany) · 2026Review
- Sociotechnical-systems analysis of IoT-AI convergence in cosmetic health.Frontiers in medicine · 2026Article
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
backgroundSkin quality has a significant influence on aesthetic perception, yet its clinical evaluation remains subjective and inconsistent. Traditional assessments, such as visual grading and manual scoring, lack reproducibility and fail to capture subtle changes over time.
aimsTo explore how artificial intelligence (AI) can transform skin quality evaluation by introducing objective, data-driven metrics that enhance precision, reproducibility, and personalization in aesthetic medicine.
methodsWe conducted a narrative review of the literature on AI-based skin analysis tools and their role in quantifying key skin quality dimensions, including pigmentation, texture, elasticity, radiance, and erythema. Emphasis was placed on the use of standardized imaging, emergent perceptual categories (EPCs), and composite scoring systems designed to capture multidimensional aspects of skin quality.
resultsAI tools enable the objective quantification of skin quality through high-dimensional image analysis, thereby reducing interobserver variability and supporting consistent evaluation across time points and populations. These systems facilitate longitudinal monitoring, tailored interventions, and patient-clinician communication. By integrating individual demographics and environmental variables, AI fosters equitable and personalized care. Regulatory and ethical considerations, such as data privacy and algorithmic bias, must be addressed to ensure the responsible implementation of these tools.
conclusionsAI represents a paradigm shift in aesthetic dermatology, offering standardized and reproducible metrics for assessing and monitoring skin quality. When aligned with validated frameworks, such as the EPCs, AI supports improved treatment outcomes, patient satisfaction, and industry-wide standardization. Future progress depends on interdisciplinary collaboration, robust regulation, and inclusive data practices.
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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.