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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.

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

This study identified m6A-related genes, particularly IGF2BP3, as significantly up-regulated in keloid patients, potentially implicating them in the condition's molecular mechanisms and suggesting targets for therapy.

The researchers reported that several m6A-related genes, particularly IGF2BP3, were differentially expressed in keloid tissue compared to normal skin, indicating potential targets for understanding keloid pathogenesis and treatment.

This study found that people with systemic lupus erythematosus consulted primary care more frequently and with specific clinical features before diagnosis, and it developed a risk prediction model that may help in identifying at-risk individuals.

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.

In this study, a modified Xception deep learning model achieved a 92% accuracy rate in diagnosing hair and scalp disorders, significantly outperforming other models, suggesting AI could improve dermatological diagnostics' accuracy and accessibility.

This review identified and evaluated polycystic ovary syndrome models of care, finding that only three fully aligned with international guidelines and generally received positive feedback on satisfaction; just one showed an impact on patients' BMI.

This study developed a model for detecting sparse hair clusters using enhanced object detection neural networks and medical images, which accurately identifies and counts sparse hair clusters with greater accuracy and efficiency than existing methods.

In this study, the DIET-AI model, developed from a large dataset of over 200,000 images, demonstrated diagnostic performance for 31 skin diseases comparable to dermatologists of varying experience levels in 15 hospitals across China, supporting its potential effectiveness in clinical settings.

This study documented that a CNN-KNN hybrid model achieved 98% accuracy in predicting hair breakage levels due to Telogen Effluvium, highlighting its potential for enhancing diagnosis and treatment in clinical dermatology through early detection of hair-related conditions.

This study developed a deep-learning model using unannotated dermatology images from online forums, achieving 49.64% accuracy in classifying 22 skin diseases and 61.76% accuracy in detecting monkeypox, highlighting the potential of these images for skin disease diagnostics in China.

This study reviewed the use of self-supervised Auto ML models for detecting alopecia areata, finding significant advancements in automated diagnosis but also challenges such as model explainability and data bias, which may guide future AI-driven dermatological diagnostics.

This review reports that while deep learning shows promise in diagnosing scalp disorders from images, challenges remain with data quality and model interpretability, suggesting that integrating explainable AI techniques is crucial for building trust and facilitating clinical adoption.

This study suggests that thermal imaging may aid in diagnosing cellulitis, potentially improving patient care, although its applicability to certain cases requires further investigation.

This study proposed and tested an Ensemble Pre-Learned Deep Learning and Optimized Long Short-Term Memory (EPL-OLSTM) model for classifying Alopecia Areata, achieving a 93.1% accuracy in differentiating healthy from varying severity levels of AA scalp hair using specific datasets.

This article reviews insights into the pathogenesis of primary cicatricial alopecias, such as lichen planopilaris, provided by emerging technologies, but it does not report new clinical results.

This narrative review found that while AI-based tools in dermatology, particularly for hair disorder assessment, have potential to enhance clinical practice by improving objectivity and personalization, they currently serve mainly a complementary role and face challenges like methodological limitations and data bias.

This review explores the potential use of plucked hair follicles in regenerative medicine and diagnostics but presents no new research findings.

Many are open to telemedicine for hair loss if combined with in-person visits and better technology.

This article reviews the epidemiology, clinical evaluation, and pathogenesis of alopecia areata and highlights recent advancements, but it does not report new clinical findings.

Hair disease research is a growing and evolving field in dermatology, with recent significant advances.

This study established an IRGDS model with diagnostic capability for alopecia areata, which may serve as an auxiliary marker for the condition.

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 found that a deep learning model can potentially improve the diagnosis and staging of alopecia areata with high accuracy and reliability.

This thesis presents a new scientific approach using specific models to gain insights into psoriasis and eczema, highlighting a molecular classifier that improves diagnostic accuracy and predicts therapeutic response for these conditions.

This study outlined modern approaches for using cosmeceuticals in trichology, emphasizing evidence-based medicine, personalized care, and patient safety. It proposed the H.A.I.R. model as a universal algorithm for trichological programs and suggested new educational and practical tools for specialists in trichology and aesthetic medicine.

The care model improved timely diagnosis and treatment for psoriasis and psoriatic arthritis.

This study found that a deep learning framework using the ResNet50 model achieved 95% overall accuracy in classifying 10 categories of hair diseases, demonstrating reliable performance but also identifying potential improvements due to misclassifications between similar conditions.

This study developed a high-performance deep learning model using the Inception-ResNet v2 architecture to classify 10 hair disease classes, achieving an accuracy of 94.7% and balanced precision, recall, and F1-scores of 0.94, suggesting reliability for automated dermatology diagnostics.