An estimated 3 billion people lack access to dermatological care globally. Artificial intelligence (AI) may aid in triaging skin diseases and identifying malignancies. However, most AI models have not been assessed on images of diverse skin tones or uncommon diseases. Thus, we created the Diverse Dermatology Images (DDI) dataset—the first publicly available, expertly curated, and pathologically confirmed image dataset with diverse skin tones. We show that state-of-the-art dermatology AI models exhibit substantial limitations on the DDI dataset, particularly on dark skin tones and uncommon diseases. We find that dermatologists, who often label AI datasets, also perform worse on images of dark skin tones and uncommon diseases. Fine-tuning AI models on the DDI images closes the performance gap between light and dark skin tones. These findings identify important weaknesses and biases in dermatology AI that should be addressed for reliable application to diverse patients and diseases.

中文翻译：

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皮肤病学 AI 在多样化、精心策划的临床图像集上的性能差异

全球估计有 30 亿人无法获得皮肤病护理。人工智能 (AI) 可能有助于对皮肤病进行分类和识别恶性肿瘤。然而，大多数 AI 模型尚未根据不同肤色或罕见疾病的图像进行评估。因此，我们创建了 Diverse Dermatology Images (DDI) 数据集——第一个公开可用、经过专业策划和病理证实的具有不同肤色的图像数据集。我们表明，最先进的皮肤病学 AI 模型在 DDI 数据集上表现出很大的局限性，特别是在深色肤色和罕见疾病方面。我们发现，经常标记 AI 数据集的皮肤科医生在深色肤色和罕见疾病的图像上表现也更差。在 DDI 图像上微调 AI 模型可缩小浅色和深色肤色之间的性能差距。