ArticleJournal of cosmetic dermatology2025
Artificial Intelligence Predicts Fitzpatrick Skin Type, Pigmentation, Redness, and Wrinkle Severity From Color Photographs of the Face.
Article 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 7 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.
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Who cites it
7 citing papers in PubMed.
- Recent advances in immunocompetent human skin-on-chip models: Construction strategies and biomedical applications.Bioactive materials · 2026Review
- Do not smile when acquiring consumer selfies for multi-attribute skin profiling and baseline deep learning evaluation.Scientific reports · 2026Article
- Limitations of ITA for Skin Type Estimation Under Uncontrolled Imaging Conditions.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
- Artificial Intelligence in Non-Surgical Cosmetic Procedures: A Multi-Stakeholder Revolution.Aesthetic plastic surgery · 2026Review
- AI-assisted quantitative analysis for evaluating melanin distribution in 3D pigmented epidermis-on-a-chip models.Frontiers in bioengineering and biotechnology · 2026Article
- Treatment of Porphyria Cutanea Tarda Scarring With Combination Laser Treatment and a Pilot Use of Artificial Intelligence to Quantify Laser Results.Journal of cosmetic dermatology · 2025Article
- 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
3 authors.
Funding
Abstract
backgroundDue to high patient demand, increasing numbers of non-dermatologists are performing skin assessments and carrying out laser interventions in medical spas, leading to inferior outcomes and higher complications. A machine learning tool that automatically analyzes patient skin has the potential to aid non-dermatologists.
aimsTo develop a high-performing machine learning model that predicts Fitzpatrick skin type, hyperpigmentation, redness, and wrinkle severity simultaneously.
methodsWe developed the SkinAnalysis dataset of 3662 images, labeled by a dermatologist across five skin scales. We trained and evaluated machine learning models across 15 different configurations, including three neural network architectures and two loss functions.
resultsThe best-performing model was an EfficientNet-V2M architecture with a custom cross entropy loss. This model's mean test set accuracy across all labels was 85.41 ± 9.86 and its mean test set AUROC was 0.8306 ± 0.09599. An interesting trend emerged in which machine learning model performance was higher at the extremes of the scales, suggesting greater clinical ambiguity in the middle of the scales.
conclusionsMachine learning models are capable of predicting multiple skin characteristics simultaneously from color photographs of the face. In the future, similar models could assist non-dermatologists in patient skin evaluation to enhance treatment planning.
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What OpenQuestion 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 OpenQuestion graph.