Revamping AI Models in Dermatology: Overcoming Critical Challenges for Enhanced Skin Lesion Diagnosis

• URL: http://arxiv.org/abs/2311.01009v1
• Date: Thu, 2 Nov 2023 06:08:49 GMT
• Title: Revamping AI Models in Dermatology: Overcoming Critical Challenges for Enhanced Skin Lesion Diagnosis
• Authors: Deval Mehta, Brigid Betz-Stablein, Toan D Nguyen, Yaniv Gal, Adrian Bowling, Martin Haskett, Maithili Sashindranath, Paul Bonnington, Victoria Mar, H Peter Soyer, Zongyuan Ge
• Abstract summary: We present an All-In-One textbfHierarchical-textbfOut of Distribution-textbfClinical Triage 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. Our versatile model provides valuable decision support for lesion diagnosis and sets a promising precedent for medical AI applications.
• Score: 8.430482797862926
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: 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.

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