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Machine learning can predict symptoms and quality of life in chronic skin disease patients using smartphone app data, and shows that app use varies with patient characteristics.

The new method improves accuracy in segmenting scalp tissue layers.

The Ewha Medical Journal is now in PubMed, has an AI article editor, and offers Korean reporting guidelines.

The system can accurately classify hair diseases with 94.5% accuracy using a CNN.

Transfer learning with three neural network architectures accurately classifies hair diseases.

AI can effectively detect hair and scalp disorders from images.

Machine learning and deep learning can effectively diagnose alopecia areata.

Self-supervised learning improves medical image classification accuracy.

AI in dermatology is growing rapidly, showing promise in diagnosing skin conditions as accurately as dermatologists.

A comprehensive human skin cell atlas was created to better understand skin biology and disease.

Computer vision techniques can help detect and assess skin conditions like vitiligo, alopecia areata, and dermatitis.

Drug repositioning can save time and money but needs more support.

A new model can predict drug-disease links well, helping drug research.

Machine learning can accurately diagnose PCOS non-invasively using clinical and ultrasound features.

Nanozymes show promise for effective and safe cancer treatment.

Using gelatin sponges for deep skin wounds helps bone marrow cells repair tissue without scarring.

AI and robotics are improving treatment and monitoring of neurodegenerative disorders like Parkinson's.

AI can improve early diagnosis and classification of PCOS, aiding in prevention of related health issues.

Machine learning can effectively predict type 2 diabetes risk.

A new tool can analyze hair to detect changes due to hormones, genetics, and aging.

Inflammatory diets may increase the risk and severity of alopecia areata.

Machine learning can accurately identify Alopecia Areata, aiding in early detection and treatment of this hair loss condition.

The system can automatically identify different hair and scalp conditions using machine learning.

Rational design strategies are crucial for developing effective nanozymes for anti-inflammatory uses.

The Hair Cell Analysis Toolbox automates and improves the analysis of cochlear hair cells using machine learning.

ATR-FTIR spectroscopy could help monitor alopecia areata treatment response non-invasively.

Reliable machine learning in medical imaging needs bias checks and data drift detection for consistent performance.

Artificial neural networks can accurately diagnose Alopecia Areata.

New technologies replicate human skin for testing without animals.

Machine learning improves DNA predictions for eye and hair color, but challenges remain for skin tone and facial features.