  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.
