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Glossary Machine Learning

using data to train algorithms for pattern recognition and decision-making

Machine Learning (ML) is a subset of artificial intelligence (AI) that involves training algorithms to recognize patterns and make decisions based on data. Instead of being explicitly programmed to perform a task, ML systems learn and improve from experience, making them useful for applications like predicting hair loss patterns in alopecia patients or identifying biological markers in complex datasets.

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Research

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research Dermacen Analytica: A Novel Methodology Integrating Multi-Modal Large Language Models with Machine Learning in tele-dermatology

3 citations , March 2024 in “arXiv (Cornell University)”

This study describes an AI-powered system for diagnosing dermatological conditions, achieving a weighted score of 0.87 in both contextual understanding and diagnostic accuracy, suggesting it could enhance tele-dermatology applications by supporting remote consultations and care access in underserved regions.

research Machine learning predicts scarring progression in lichen planopilaris: A multidimensional model integrating trichoscopy, vitamin D, and diagnostic timelines

September 2025 in “International Journal of Medical Informatics”

A machine learning model can predict scarring in lichen planopilaris using factors like vitamin D levels and diagnostic delay.

research LB1040 Machine learning-based predictive model with routine blood work identifies moderate-severe alopecia areata

July 2024 in “Journal of Investigative Dermatology”

Machine learning can use blood tests to help predict moderate-to-severe alopecia areata.

Early Prediction of Alopecia Areata Using Machine Learning Modeling of Neuro Stress Immune Signatures from Multiple Datasets

research Early prediction of alopecia areata using machine learning modeling of neuro stress immune signatures from multi datasets

1 citations , December 2025 in “Scientific Reports”

In this study, researchers developed a predictive model for the onset of alopecia areata by analyzing six datasets to identify key feature genes and employing various machine learning algorithms, ultimately finding the XGBoost model most effective for clinical application.

research Identification of Stress-Related Hair Loss and Prevention Through the Application of a KNN Model Based on Machine Learning

August 2025 in “International Journal of Research Publication and Reviews”

This study suggests that stress intensity is highly correlated with hairfall severity, highlighting the potential of an inexpensive and accessible machine learning approach for forecasting and prevention.

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community Baldness Breakthrough: microRNA Stimulates Hair Growth in Aging Follicles

316 upvotes 3 years ago

miR-205, a tiny RNA, can stimulate hair growth by softening aging hair follicle stem cells in mice. Future tests aim to see if this can work in humans.

community Hair loss research is a money grab

227 upvotes 10 months ago

Hair loss is linked to DHT, with treatments like finasteride and minoxidil being common but not definitive. Economic interests influence research, and there is potential for new treatments like PP405 and RU58841.

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