用视觉语言模型自动评估湿疹严重程度，提升诊断可解释性。
VLM Models and Automated Grading of Atopic Dermatitis
- 采用7种视觉语言模型分析湿疹图像的严重程度。
- 验证了VLM在医学图像评估中的可行性和准确性。
- 适合关注医疗AI可解释性的研究者和临床医生参考。
从患者皮肤图像中对特应性皮炎（AD，一种湿疹）进行分级，即使对受过训练的皮肤科医生而言也极具挑战。近年来，随着深度学习的发展，该任务的自动化研究取得进展；然而，多模态模型尤其是视觉-语言模型（VLMs）的快速演进，为医学图像（包括皮肤病学）的可解释性评估带来了新可能。本报告描述了针对一组测试图像开展的实验，评估了七种VLM在评估AD严重程度方面的能力。
原文摘要 · Abstract (English)
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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