The surge in developing deep learning models for diagnosing skin lesions through image analysis is notable, yet their clinical black faces challenges. Current dermatology AI models have limitations: limited number of possible diagnostic outputs, lack of real-world testing on uncommon skin lesions, inability to detect out-of-distribution images, and over-reliance on dermoscopic images. To address these, we present an All-In-One \textbf{H}ierarchical-\textbf{O}ut of Distribution-\textbf{C}linical Triage (HOT) model. For a clinical image, our model generates three outputs: a hierarchical prediction, an alert for out-of-distribution images, and a recommendation for dermoscopy if clinical image alone is insufficient for diagnosis. When the recommendation is pursued, it integrates both clinical and dermoscopic images to deliver final diagnosis. Extensive experiments on a representative cutaneous lesion dataset demonstrate the effectiveness and synergy of each component within our framework. Our versatile model provides valuable decision support for lesion diagnosis and sets a promising precedent for medical AI applications.

翻译：通过图像分析诊断皮肤病变的深度学习模型开发热潮显著，但其临床落地仍面临挑战。当前皮肤病学AI模型存在以下局限：可诊断输出类别有限、缺乏针对罕见皮肤病变的真实场景测试、无法检测分布外图像、过度依赖皮肤镜图像。为解决这些问题，我们提出一种全能型分层-分布外-临床分诊（HOT）模型。对于临床图像，我们的模型生成三个输出：分层预测、分布外图像警报，以及当仅凭临床图像不足以诊断时推荐皮肤镜检查的建议。当采纳此推荐时，模型会整合临床图像与皮肤镜图像以提供最终诊断。在代表性皮肤病变数据集上的广泛实验证明了框架内各组件的有效性与协同性。这一多用途模型为病变诊断提供了有价值的决策支持，也为医学AI应用奠定了有前景的范例。