ArticleNPJ digital medicine2025
Beyond Fitzpatrick: automated artificial intelligence-based skin tone analysis in dermatological patients.
Article in NPJ digital medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
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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
3 citing papers in PubMed.
- A deep learning framework for recognizing skin changes secondary to chronic venous insufficiency in clinical photographs: a multicentre validation study.Annals of medicine · 2026Article
- A National Exploration of Medical Students' Views Surrounding Skin Color Terminology in Undergraduate Education.JMIR medical education · 2026Article
- Artificial intelligence in three-dimensional total-body photography for skin cancer surveillance.Frontiers in medicine · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Human skin tone is influenced by genetic, environmental, and cultural factors and plays a key role in dermatology due to variation in disease presentation across skin tones. The widely used Fitzpatrick scale, based on UV response, classifies only a small number of skin types, limiting its ability to capture the full diversity of skin tones. This study introduces an algorithm for automated skin tone assessment by calculating the Individual Typology Angle (ITA) from CIELAB color values using DensePose and OpenFace. ITA values are mapped to both Fitzpatrick and Monk skin tone scales. Validation on 3D body scans and AI-generated images showed high agreement with Monk classifications but less consistent alignment with Fitzpatrick types. Despite class imbalance, the algorithm reliably classifies skin tone to the Monk scale and holds potential for applications in teledermatology, clinical research, and personalized medicine. Further research is warranted to externally validate our algorithm.
Identifiers
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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.