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This study discusses the development of deep learning models for diagnosing skin disorders and notes challenges such as lack of data for darker skin tones, without providing new clinical results.

This study reports that the ScalpEye system accurately diagnosed dandruff, folliculitis, hair loss, and oily hair with a precision range of 97.41% to 99.09%.

This study found that a random forest algorithm most effectively detected COVID-19, with high specificity and accuracy, among 10,862 individuals in an Iranian hospital setting.

This review examines advancements in deep learning methods for detecting dermatological conditions from dermoscopic images, summarizing available datasets and suggesting future research directions, but reports no new results.

This study reports that an Explainable AI-based hair health prediction system using a novel hybrid model outperformed traditional machine learning methods, achieving high accuracy in predicting key factors and providing personalized recommendations.

This paper presents a new method using Convolutional Neural Networks for detecting hair and scalp diseases, aiming to enhance diagnostics accessibility through a web-based platform integration.

This study developed a method using Convolutional Neural Networks to detect nutritional deficiencies, such as iron, zinc, biotin, and vitamins, through high-resolution images of hair and nails, achieving an 89% accuracy rate in identifying these deficiencies.

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 highlights the need for personalized hair care plans based on scalp type and environmental factors, underscoring the importance of targeted product selection and the involvement of professionals to create effective solutions for modern lifestyle issues.

This study highlights the importance of creating customized, evidence-based hair care solutions by considering scalp types and environmental factors, confirming the effectiveness of multidisciplinary approaches integrating dermatology, cosmetology, and trichology to develop safe and efficient products and treatments.

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 study used in vivo confocal microscopy and a ResNet34 deep learning model to classify meibomian gland images with an AUROC greater than 0.95, indicating its potential for automatic diagnosis and screening of meibomian gland dysfunction.

This study developed a deep learning-based tool to quantify individual hair fibers in mice, revealing distinct hair phenotypes linked to hormonal, genetic, and age-related factors, and suggesting its potential for new diagnostic methods through hair analysis.

This review highlights current and future AI applications in hair restoration and diagnosis of hair disorders, including automated systems for hair detection and self-diagnosis, emphasizing the need for experts to understand their benefits and limitations.

In this study, researchers developed a deep learning model that efficiently classifies five degrees of harm with high accuracy, achieving up to 98% precision, recall, and F1-score across various harm levels, indicating strong potential for practical application in automated harm evaluation.

This study introduced a deep learning framework combining multiple convolutional neural networks to detect scalp and hair disorders and classify hair fall stages, reporting higher precision and robustness in detection and classification compared to individual CNN models.

This review examines the use of computer vision techniques, specifically deep learning architectures and image processing algorithms, for detecting and assessing skin conditions like vitiligo and dermatitis, and highlights the need for disease-specific datasets to improve automated diagnostic tools in dermatology.

This study reported that a new deep learning algorithm using Mask R-CNN improved hair follicle classification accuracy by 4 to 15%, suggesting potential clinical application for enhanced hair loss diagnosis.

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 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 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 developed a deep learning framework and quantitative model that accurately predict basic and specific classification in male androgenetic alopecia by analyzing trichoscopic images.

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

HairLossMultinet accurately classifies hair damage with 98% accuracy but needs a more diverse dataset for broader use.

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 research explored the use of machine learning and deep learning methods to accurately identify alopecia areata in humans by analyzing facial images and demonstrated the potential of these techniques for medical, security, and commercial applications.

This study explored deep learning's potential in diagnosing scalp conditions like alopecia, psoriasis, and folliculitis, using a two-dimensional Convolutional Neural Network, achieving high accuracy and precision despite challenges of a small and uneven dataset.

This scoping review describes the current use of artificial intelligence in alopecia research, highlighting AI's evolution from diagnostic to prognostic applications in dermatology and identifying gaps in multimodal integration and fairness across demographics.

In this study, researchers developed a novel computational framework using deep reinforcement learning to identify strategies for cellular reprogramming in gene regulatory networks, showing its effectiveness in a model of immune response against infection.

This study evaluated different convolutional neural network architectures for diagnosing scalp and hair diseases, and found that VGG16 and VGG19 consistently outperformed other models in accuracy, demonstrating their effectiveness and reliability in this medical application.