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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 method creates realistic, anonymous acne face images for research, achieving 97.6% accuracy in classification.

Medical practitioners need to understand basic statistics to properly evaluate clinical trials and avoid unethical designs.

The study found that combining different databases gives a better estimate of drug side effects in hospitals.

The document concludes that an automatic system using deep learning can help diagnose skin disorders, but challenges and opportunities in this area remain.

Doctors need to understand statistics to properly evaluate clinical trials for patient care.

Iron deficiency anemia may contribute to hair loss in women with Telogen effluvium.

Anifrolumab improves quality of life and reduces steroid use in lupus patients.

Two women lost hair after mesotherapy for hair loss, with one having permanent and the other temporary hair loss.

Men taking dutasteride or finasteride have a slightly higher risk of developing type 2 diabetes.

Healthcare claims databases can help monitor drug safety, but may report adverse events differently than direct surveillance.

Children with different rheumatologic diseases have specific skin symptoms that can help with diagnosis.

Deep learning helps detect skin conditions and is advancing dermatology diagnosis and treatment.

AnnoPharma effectively identifies substances causing adverse drug reactions in medical abstracts.

The document concludes that lifestyle changes and medical treatments can significantly reduce symptoms of Hidradenitis Suppurativa, a chronic skin condition.

The analysis found that alopecia areata, a hair loss condition, is not very common in Australia, affecting about 0.13% of people, with new cases most often seen in males aged 19 to 34 years.

The article suggests that the view of male baldness as a medical issue is influenced by commercial bias and calls for more unbiased research.

Botulinum toxin's effectiveness for treating scalp alopecia is not well-supported due to insufficient data.

The article suggests using four questions to tell apart necessary medicalization from excessive medicalization, focusing on problem significance, social expectations, medical understanding, and effective resolution.

Comprehensive screening for infections is crucial before starting JAK inhibitors in alopecia areata patients.

Scalp involvement is common in pemphigus and can lead to hair loss, with the severity of scalp lesions linked to overall disease severity.

AI-assisted surgical robots improve surgery precision and safety.

More research is needed on CCCA in children, especially Black and Asian adolescents.

AA patients with comorbid conditions face more severe hair loss and need specific treatments.

The model and estimator can predict drug exposure in kidney transplant patients well.

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

There is no gold standard treatment for hidradenitis suppurativa due to insufficient evidence.

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

Pica disorder in central Iraq is mainly found in females and is linked to low iron levels; treatment with iron improves most patients.

Ginseng is possibly safe but its effectiveness is unclear due to poor quality studies and mixed results.