VLM Models and Automated Grading of Atopic Dermatitis

The task of grading atopic dermatitis (or AD, a form of eczema) from patient images is difficult even for trained dermatologists. Research on automating this task has progressed in recent years with the development of deep learning solutions; however, the rapid evolution of multimodal models and more specifically vision-language models (VLMs) opens the door to new possibilities in terms of explainable assessment of medical images, including dermatology. This report describes experiments carried out to evaluate the ability of seven VLMs to assess the severity of AD on a set of test images.

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Publication Details

Journal
ArXiv.org
Published
2025-05-23
Primary Topic
Dermatology and Skin Diseases
Type
article
Field-Weighted Citation Impact
0.00

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article

VLM Models and Automated Grading of Atopic Dermatitis

ArXiv.org
Dermatology and Skin Diseases
article

VLM Models and Automated Grading of Atopic Dermatitis

article en

Abstract

The task of grading atopic dermatitis (or AD, a form of eczema) from patient images is difficult even for trained dermatologists. Research on automating this task has progressed in recent years with the development of deep learning solutions; however, the rapid evolution of multimodal models and more specifically vision-language models (VLMs) opens the door to new possibilities in terms of explainable assessment of medical images, including dermatology. This report describes experiments carried out to evaluate the ability of seven VLMs to assess the severity of AD on a set of test images.

ArXiv.org
Computer Research Institute of Montréal (CA)
Ministère de l'Économie, de l’Innovation et des Exportations du Québec, Université de Montréal
Quality Education
Openalex Percentile: Top 32%
Dermatology and Skin Diseases
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VLM Models and Automated Grading of Atopic Dermatitis · ArXiv.org (2025) | TGRS Research Map | TGRS