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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.

In this study, VGG19 slightly outperformed MobileNetV2 in hair disease classification accuracy, achieving 98% compared to MobileNetV2's 97%. However, MobileNetV2 was faster and more computationally efficient, making it suitable for resource-limited settings.

This study proposes a deep learning model using CNN with VGG16, VGG19, and MobileNetV2 architectures, achieving high accuracy in classifying scalp diseases from images, potentially facilitating diagnosis and treatment via mobile devices.

A deep learning model was developed to help diagnose trichothiodystrophy by analyzing hair patterns.

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 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 used bibliometric analysis to evaluate global research on AI applications in dermatology, identifying 406 relevant papers and highlighting current priorities such as machine learning for wound progression, AI in teledermatology, and applications for skin diseases.

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.

In this study, researchers evaluated deep learning methods for diagnosing Alopecia Areata and found that a modified Inception-Resnet-v2 model achieved a high validation accuracy of 97.94% and loss of 10.4%, suggesting it as an effective tool for classifying alopecia-affected hair.

This study developed a deep-learning model that accurately diagnosed alopecia areata with an accuracy of 88.92% and distinguished its activity levels with an accuracy of 83.33%, highlighting the potential for artificial intelligence in improving the diagnosis and treatment of this autoimmune hair loss condition.

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.

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

In this study, researchers proposed a deep learning approach combining genetic, hormonal, scalp health, and lifestyle data to predict hair loss, employing CNNs for image analysis and RNNs for modeling data over time, although specific results are not reported.

This study identified three genetic loci influencing hair density in East Asian populations and found associations with demographic and lifestyle factors like age, sex, and BMI. The results also suggest possible genotype-specific responses to finasteride for managing hair disorders.

This study describes the PandaOmics platform, which uses AI and bioinformatics to identify new therapeutic targets and biomarkers for various diseases, demonstrating validation in laboratory and animal studies.

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.

In this study, the BaldGraphFormer framework, integrating visual and clinical data, outperformed unimodal baselines in early-stage androgenetic alopecia detection, achieving an F1-score of 97.62% and macro-average AUC of 0.992, suggesting its potential to support dermatological decision-making and early intervention.

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 study introduced Neurospectrum, a framework that effectively identifies meaningful neural dynamics by encoding neural activity into latent trajectories, and reported that it outperformed traditional methods in tracking synchronization, reconstructing stimuli, and identifying fMRI biomarkers in various datasets.

This study introduces the Hair Cell Analysis Toolbox (HCAT), a machine-learning software that automates the analysis of cochlear hair cells, enabling unbiased and comprehensive imaging data interpretation.

In this study, researchers developed an AI-powered platform called VitaDetect, which screens for vitamin deficiencies using image analysis of nails, tongue, and skin, aiming to provide an accessible and early-stage detection tool in resource-limited settings.

In this scoping review, researchers observed that while AI-assisted trichoscopy holds promise for standardized assessments of hair and scalp disorders, its clinical translation is limited by small proprietary datasets, inconsistent validation protocols, and a scarcity of real-world clinical studies.

This article reviews the steps and methods involved in preparing skin tissues for three-dimensional volumetric imaging, highlighting its potential to provide detailed insights into skin structure not possible with traditional 2D histology, but reports no new findings.

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 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 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 AI using YOLOv11 architecture can reliably differentiate between hyaluronic acid and silicone oil cosmetic fillers on ultrasound, achieving high accuracy, whereas identifying calcium hydroxyapatite and polymethylmethacrylate remains less consistent, requiring further improvements.

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 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.