Access to dermatological care is a major issue, with an estimated 3 billion
people lacking access to care globally. Artificial intelligence (AI) may aid in
triaging skin diseases. However, most AI models have not been rigorously
assessed on images of diverse skin tones or uncommon diseases. To ascertain
potential biases in algorithm performance in this context, we curated the
Diverse Dermatology Images (DDI) dataset-the first publicly available, expertly
curated, and pathologically confirmed image dataset with diverse skin tones.
Using this dataset of 656 images, we show that state-of-the-art dermatology AI
models perform substantially worse on DDI, with receiver operator curve area
under the curve (ROC-AUC) dropping by 27-36 percent compared to the models'
original test results. All the models performed worse on dark skin tones and
uncommon diseases, which are represented in the DDI dataset. Additionally, we
find that dermatologists, who typically provide visual labels for AI training
and test datasets, also perform worse on images of dark skin tones and uncommon
diseases compared to ground truth biopsy annotations. Finally, fine-tuning AI
models on the well-characterized and diverse DDI images closed the performance
gap between light and dark skin tones. Moreover, algorithms fine-tuned on
diverse skin tones outperformed dermatologists on identifying malignancy on
images of dark skin tones. Our findings identify important weaknesses and
biases in dermatology AI that need to be addressed to ensure reliable
application to diverse patients and diseases.

本文提出了困扰全球 30 亿人的皮肤科医疗问题，探讨了如何在多样化皮肤色调和非常见病例的图像中应用人工智能技术，并发现当前的皮肤科 AI 算法存在严重的算法偏差，导致对较暗的肤色和非常见疾病的正确率远低于轻皮肤色调和常见疾病，并进一步探讨了训练皮肤科 AI 模型所使用的基于视觉的人工标签的缺陷与局限性。