Dermacen Analytica: A Novel Methodology Integrating Multi-Modal Large Language Models with Machine Learning in Tele-Dermatology

This study reports that publicly available large language models are currently less accurate than human experts in diagnosing trichoscopic images, suggesting the need for further development and specialized training for these AI tools in trichology.

This review outlines the key principles and clinical applications of multimodal non-invasive skin imaging technologies, emphasizing their combined strengths in enhancing diagnosis and treatment assessment in dermatology.

This review examines the use of computer vision techniques, specifically deep learning architectures and image processing algorithms, for detecting and assessing skin conditions like vitiligo and dermatitis, and highlights the need for disease-specific datasets to improve automated diagnostic tools in dermatology.

This article describes current advancements and future possibilities in skin imaging technology, teledermatology, and AI in dermatology, without reporting new research results.

In this study, researchers developed ScalpViT, a novel deep learning model, to improve the automated diagnosis of visually similar scalp diseases, achieving 94.3% accuracy and outperforming existing models like ResNet-50 and EfficientNet-B3 when tested on a diverse dataset of 7,000 images.