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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.

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

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

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.

This study evaluated machine-learning models to predict PCOS among reproductive-aged women in Bangladesh, finding that the XGBoost model achieved high accuracy (99.63%) and effectiveness, particularly when prioritizing clinical features over psychological ones in the predictive process.

This study found that using machine learning models, particularly Random Forest with 93% accuracy and 86% sensitivity, can effectively predict PCOS by analyzing features like antral follicle count, hair growth, and skin pigmentation, offering a promising alternative to traditional diagnostic methods.

This review highlights recent research using single-cell RNA sequencing and machine learning in wound healing, revealing significant insights into fibroblast diversity, immune cell dynamics, and the spatial organization of cells, which may transform therapeutic strategies for chronic wounds, fibrosis, and tissue regeneration.

This study reported that AutoML effectively modeled itching and pain development, as well as app use, in patients with chronic eczema or psoriasis using a smartphone monitoring app, revealing that factors like BMI, age, and disease activity significantly influenced app engagement.

This study proposed a model that accurately predicts skin condition using genotype information and machine learning, suggesting potential for creating customized cosmetics.

This study reports that deep learning models, particularly CNN and FCN, achieved high accuracy in diagnosing scalp and skin disorders, suggesting potential for improved diagnostic systems with further advancements.

This study developed a support vector machine model using trichoscopic patterns to accurately classify androgenic alopecia severity, with an accuracy of 94.3% in training and 90.0% in test datasets.

This study used machine learning to develop classifiers for identifying effective inhibitors of 5α-reductase isozyme 2, achieving high performance in distinguishing potent from weak inhibitors.

In this study, machine learning-based computer-aided diagnosis significantly improved accuracy in diagnosing alopecia areata compared to traditional visual methods, achieving up to 91.9% accuracy using different classifiers like CNN, SVM, and random forest models.

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.

This review highlights the global efforts and challenges in 3D bioprinting for developing skin substitutes, emphasizing the need for standardized protocols to enhance reproducibility and clinical applicability in wound healing and regeneration.

This study developed the CATNIP computational model, which uses biological and chemical information to successfully identify drug repurposing opportunities for various conditions, including Parkinson’s disease and Type 2 Diabetes.

In this study, researchers evaluated various machine learning models for predicting type 2 diabetes risk, finding that Random Forest and K-NN models performed best in terms of precision, recall, accuracy, and other metrics using common symptoms as features.

This study developed a deep learning model using the VGG architecture to predict hair disorders and provide tailored therapeutic suggestions, showing reliable recognition of conditions like dandruff, fungal infections, and alopecia by analyzing images of hair and scalp.

This study found that an ML model incorporating factors like Breslow thickness and age improved cutaneous malignant melanoma prognosis predictions compared to TNM staging, with a C-index of 80% versus 66.6% for TNM alone, suggesting ML's potential for personalized prognostication.

In this study, researchers identified genes IRF2BP2 and EGFR as key to understanding double-coated fleece formation in Hetian sheep, offering insights that may advance machine learning-driven multi-omics selection models in sheep breeding.

This study aims to develop an automatic machine learning-based method using the VGG-19 model to accurately classify various hair and scalp diseases.

This study used multi-omics analysis to explore how wheat roots adapt to temperature stress, revealing that distinct hormonal signaling pathways are linked to iron homeostasis and drive root morphological changes, supporting the idea of an 'iron-dependent hormonal trade-off' model.

This study reports that using machine learning models, particularly XGBoost and Random Forest, can accurately predict PCOS phenotypes based on non-invasive data, with cycle length as the most significant predictor.

This review discussed recent research on using machine learning to predict mental disorders, reporting that Adaboost could predict depression with 92.5% accuracy and 93.6% specificity, while other models like XGBoost and RNN were applied for post-stroke depression and EEG-based depression detection, respectively.

This study identified four ferroptosis-related genes, SLC40A1, LCN2, CREB5, and SLC7A11, as potential diagnostic markers for alopecia areata, revealing reduced expression in affected patients compared to controls, with a predictive model showing high accuracy in differentiating the condition.

The authors of this study developed a novel CNN architecture aimed at improving detection of Alopecia Areata through image-based datasets, achieving a top accuracy of 98% compared to four other machine learning models.

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

Innovative methods are reducing animal testing and improving biomedical research.

This study found that a combined machine learning model outperformed individual networks in diagnosing scalp conditions using visual data, enhancing prediction accuracy and early detection, particularly in settings with limited resources.

In this study, a machine-learning model was evaluated for its ability to categorize various hair conditions, achieving high accuracy and balance between precision and recall, with an overall accuracy of 97% in detecting hair problems.