VLM Models and Automated Grading of Atopic Dermatitis
This work addresses the problem of automating dermatological assessments for medical professionals, but it appears incremental as it applies existing VLM methods to a specific domain.
The study evaluated seven vision-language models (VLMs) for grading atopic dermatitis severity from patient images, aiming to automate this challenging medical task.
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.