ATOMIC: A Gut-Microbiome Model That Predicts Atopic Dermatitis
A Frontiers study presents ATOMIC, a graph attention network that predicts atopic dermatitis from human gut microbiome data — a proof of concept for computational risk stratification.
Topic
Machine Learning
Topic
Machine Learning
DESI-MSI machine learning reaches 94.71% pixel accuracy for melanoma
DESI-MSI plus machine learning flagged melanoma with 90.24% lesion-level and 94.71% pixel-level accuracy across 137 specimens, wi…
Hybrid Optimizer Sharpens Deep Learning's Eye for Skin Cancer
A hybrid optimization strategy improves how deep learning models classify skin cancer, Bioengineer.org reports, shifting focus fr…
Attention-Guided Fair AI Modeling for Skin Cancer Diagnosis Published in Nature
A Nature publication introduces attention-guided fair artificial intelligence modeling for skin cancer diagnosis, aiming to reduc…
Machine Learning Maps Environment and Immunity in Childhood Eczema Risk
A machine learning study maps how environmental exposures and immune pathways jointly shape childhood eczema risk, moving beyond…
AI Coupled With Raman Spectroscopy Improves Skin Cancer Detection
Artificial intelligence paired with Raman spectroscopy improves skin cancer detection, reading the biochemical signature of lesio…
Foundation Models Usher In a New AI Era for Dermatology
Medscape charts dermatology AI's shift from task-specific CNNs to foundation models — adaptable, multimodal systems that could mo…
Machine Learning Predicts Hyperglycemia Risk in Psoriasis
A machine learning model can identify psoriasis patients at elevated risk of hyperglycemia, AJMC reports, pointing dermatology to…