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

New imaging technologies improve skin diagnosis but face cost and training challenges.

Deep learning can accurately automate hair density measurement, with YOLOv4 performing best.

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

AI can accurately measure hair loss severity in alopecia areata.

The method accurately detects and classifies scalp diseases, including alopecia areata, with 89.3% accuracy.

Artificial neural networks can accurately diagnose Alopecia Areata.

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

AI is effective in diagnosing and treating hair disorders, including detecting hair loss and scalp conditions with high accuracy, but it should supplement, not replace, doctor-patient interactions.

3D bioprinting shows promise for creating skin substitutes, but standardized methods are needed for clinical use.

A new algorithm effectively classifies Alopecia Areata, aiding early detection and treatment.

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

A new AI model diagnoses hair and scalp disorders with 92% accuracy, better than previous models.

The AI system accurately classifies hair loss types and explains its decisions.

The tool accurately tracks eyebrow hair loss in chemotherapy patients.

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.

Deep-learning models can effectively diagnose and assess Alopecia areata using scalp images.

AI in alopecia research needs better tools for predicting treatment outcomes and ensuring fairness.

Personalized hair care using modern techniques and science is essential for healthy hair.

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

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

AI can effectively detect hair and scalp disorders from images.

Personalized hair care plans are essential for healthy hair.

AI can improve alopecia areata diagnosis with high accuracy.

5-alpha reductase inhibitors and spironolactone don't increase gynecological tumor risk, glucagon-like-peptide-1 receptor agonists may increase alopecia risk, sanitary pads can cause dermatitis, early hidradenitis suppurativa intervention is crucial for Hispanic patients, community education boosts skin cancer awareness, metformin may improve skin cancer survival, LNG-IUDs can increase acne risk, machine learning accurately detects basal cell carcinoma, JAK-inhibitors may raise miscarriage risk, inclusive skincare ads affect those with darker skin, and CCCA in Black women might involve autoimmunity.

Age, sex, BMI, menopause, and specific genes affect hair density in East Asians.

The optimized VGG19 model accurately classifies hair diseases with 98.64% accuracy.

Deep learning can improve non-invasive alopecia diagnosis using hair images.

The Scalp Coverage Scoring method reliably measures hair density from images.