Multi-Dataset Diagnostic Utility of Clinical Visual Concepts in AI Systems for Dermatology
The clinical integration of AI systems in digital dermatology relies heavily on human trust. Clinically interpretable visual concepts can act as intermediate representations enhancing trust and reliability. However, research in this domain is currently limited by scattered, heterogeneous dataset annotations. In this work, we introduce SkinLex, a harmonized dataset of 48 clinical morphological attributes across four public datasets (SkinCon, DermaCon-IN, MM-Skin, and PASSION) for a total of 20,411 records. Supervised nine-partition classification of skin conditions shows that limiting features to specific visual groups, like shapes or colors alone, reduces diagnostic accuracy. Bootstrapped...
| Authors | Linda Wermelinger, Simone Lionetti, Fabian Gröger et al. |
|---|---|
| Primary category | cs.CV |
| Published | 6 October 2026 |
| Last updated at source | 6 October 2026 |
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