Bridging Research and Practice: A Systematic Evaluation of Generalist and Dermatology-Specific Models in Clinical Skin Lesion Classification

2026-10-02

AI Summary

This study addresses the insufficient model robustness and clinical deployment challenges in skin lesion classification arising from data heterogeneity. We present the first systematic comparative analysis between cross-modal foundation models and task-specific classifiers. Methodologically, by integrating vision-language models, convolutional neural networks, and feature embedding techniques, this work comprehensively evaluates the performance discrepancies between general-purpose and specialized models under distribution shifts while quantifying their generalization gaps. Our findings reveal the inherent limitations of existing state-of-the-art models, providing a critical benchmark and empirical evidence for developing safe, equitable, and trustworthy AI systems in dermatology.

Abstract

The application of machine learning to dermatology has grown substantially in recent years, moving beyond proof-of-concept studies toward potential applications. However, clinical dermatology remains a challenging and still open problem. Diagnostic assessment is often ambiguous, and skin lesions exhibit high variability, compounded by differences in acquisition modality, device quality, and patient demographics. These factors hinder the development of robust models suitable for safe and equitable clinical use. To support translation into practice, it is essential to systematically evaluate how contemporary models generalize across heterogeneous data sources. In this work, we benchmark a diverse set of architectures on recent dermatology datasets, spanning dermoscopic images and smartphone-based clinical photographs. We assess the robustness of recent general-purpose and medical vision-language models, as well as foundation models, and compare them against task-specific dermatology classifiers, including embedding-based approaches and convolutional neural networks. Our study provides an evaluation of model performance under distribution shifts, modality changes, and demographic variability. By quantifying the gap between current state-of-the-art models and the requirements of clinical deployment, we aim to contribute to the development of reliable, accessible, and clinically applicable AI systems for dermatology.

Problem

• clinical dermatology
• skin lesion classification
• model robustness
• distribution shift
• vision-language models

Innovation

• Vision-Language Models
• Foundation Models
• Dermatology Classification
• Distribution Shifts
• Systematic Benchmarking