Revamping AI Models in Dermatology: Overcoming Critical Challenges for Enhanced Skin Lesion Diagnosis
This work addresses critical challenges in skin lesion diagnosis for clinical applications, representing an incremental improvement by integrating multiple components into a single framework.
The paper tackled the problem of limited diagnostic outputs, lack of real-world testing, inability to detect out-of-distribution images, and over-reliance on dermoscopic images in dermatology AI models by introducing the HOT model, which generates hierarchical predictions, alerts for out-of-distribution images, and recommendations for dermoscopy, demonstrating effectiveness on a cutaneous lesion dataset.
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.