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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 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 proposes a machine learning framework for classifying healthy hair and alopecia areata using image processing and classification techniques, but does not report new clinical findings.

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.

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.

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 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 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.

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 analyzed deep learning's application to automate hair density measurement in images and found that YOLOv4 had the best performance among tested algorithms, with a mean average precision of 58.67.

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 introduces PROMETHEUS, a framework that organizes causal claims from scientific texts into navigable and persistent "causal atlases," enhancing research by highlighting localized evidence, agreement, and contradictions within complex data sets.

This source reports that clinical trials using advanced Deep Brain Stimulation systems, augmented with AI to integrate kinematic data, eye tracking, and cognitive assessments, show promise in improving diagnostic accuracy and monitoring symptoms for patients with Parkinson's disease.

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 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 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.

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 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.

This study found that a deep learning approach successfully predicted three types of hair and scalp diseases with high accuracy, despite challenges in dataset availability and image variety.

This review discusses the developments over the past five years in nanozyme-based theranostics for tumor therapy, including their classification, design, and synergistic strategies. It also outlines the challenges and prospects of using nanozymes to enhance selectivity, biosafety, repeatability, and stability in therapeutic applications.

This study evaluated a novel, user-friendly approach for detecting hairfall trends over time using machine learning models. The Temporal Fusion Transformer model demonstrated high accuracy in identifying anomalies in hair shedding patterns, potentially aiding in the early detection of health risks related to hormonal fluctuations.

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 study introduces a novel method for digitizing hair color that accurately captures and renders the color appearance of physical hair samples in synthetic images.

This study evaluates a framework using neural networks and machine learning techniques to classify and detect Alopecia Areata from hair images, aiming for accurate differentiation between healthy hair and the condition.

This study introduces a novel computational model using fuzzy logic and multi-criteria decision-making techniques to create a triage system for androgenetic alopecia management, stratifying patients into seven severity levels and aiding in resource allocation and treatment planning.

In this review of autonomous robotic surgery, the authors explore the integration of AI and machine learning in surgical procedures, detailing both the advancements and challenges of these technologies, including ethical concerns and current regulatory frameworks.

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 study introduced a novel framework called SL-HyDE that significantly improved zero-shot dense retrieval accuracy in medical information retrieval without relying on labeled data.

This study found that ensemble machine learning models effectively predict hair fall by combining the strengths of individual algorithms, leading to higher accuracy, precision, and recall in identifying hair and non-hair fall instances compared to single algorithms.