Artificial Intelligence Techniques for Diagnosing and Quantifying Vitiligo: A Systematic Review and Meta-Analysis
In brief
AI models scored above 0.90 for vitiligo detection, often matching experts
A review of 54 studies found imaging-based AI models had area-under-the-curve scores above 0.90 for diagnosis and a Dice score of 0.84 for measuring lesions. In comparisons, models often performed as well as human experts, but limited dataset diversity and a lack of full-body image studies leave their readiness for routine clinical use uncertain.
- Journal
- The Journal of investigative dermatology (Q1)
- Published
- 5 October 2026
- Study design
- Systematic review of cohort studies
- Evidence level
- Level 2, Moderate (CEBM 2a)
- Authors
- Nneka Ede, Fatima Khan, Imelda Vetter, Camryn Schroeder, Ammar M Ahmed
- PMID
- 42833397
- DOI
- 10.1016/j.jid.2026.09.853
Why clinicians should know about it
- Picked for Dermatology (paper of the day, 10 October 2026): Systematic review, AI improves vitiligo diagnosis accuracy
Abstract
The utilization of machine learning (ML) and deep learning (DL) for vitiligo, particularly in image and object recognition, allows for the creation of innovative techniques that may match the precision and accuracy of current clinical methodologies. This systematic review seeks to explore how imaging-based artificial intelligence (AI) models may improve the accuracy of vitiligo diagnosis, severity assessment, and monitoring. A comprehensive literature search was conducted across PubMed, IEEE Xplore, EMBASE, Compendex, and Cochrane. Fifty-four studies from January 1, 2000, to October 10th, 2024, focusing on vitiligo diagnosis, classification, treatment, or disease severity were included in this review. Meta-analysis showed models achieved high diagnostic accuracy with an area under the curve of over 0.90 and lesion segmentation with a Dice score of 0.84. Often results matched human expert performance. Studies comparing AI models with experts reported AI could perform equally to that of human experts. These studies highlight the ability of ML models to accurately detect vitiligo and provide quantitative data on the extent of lesions. While AI has shown considerable potential for the improvement of vitiligo management, larger, more diverse publicly available vitiligo datasets and studies focusing on vitiligo segmentation with full-body images are needed for full clinical integration.
Abstract as published, via PubMed.
For healthcare professionals. The summary is generated by AI from the published abstract, and the evidence level is assigned automatically from the study design on the Oxford CEBM hierarchy. Neither is medical advice. Read the full paper before changing practice.