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AI can accurately measure hair loss severity in alopecia areata.

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 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.

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.

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

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.

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 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 found that an Attention-based Balanced Multi-Task Deep learning system achieved a 95.11% accuracy in classifying Alopecia Areata conditions using hair and scalp images, outperforming classical methods.

In this study, the researchers used machine learning techniques on an image dataset to diagnose Alopecia Areata, achieving a maximum accuracy of 98.3%.

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.

In this study, the researchers developed a deep learning framework to pre-emptively screen for adverse drug effects, showing strong predictive performance, including for increased bleeding risks with edoxaban compared to other anticoagulants.

This study introduced the KRDQN predictive framework, which outperformed existing methods in predicting adverse drug reactions and provided interpretable insights into drug mechanisms, aiding pharmacovigilance and clinical decision-making.

In this study, a deep learning model using an optimized VGG19 architecture achieved a high classification accuracy of 98.64% for detecting ten hair disease classes from a balanced dataset, indicating its potential for reliable use in mobile diagnostics for clinical and remote applications.

This study demonstrated that a CNN-based model using VGG-16 architecture achieved a 94.5% accuracy in classifying ten types of hair diseases, implying a promising tool for aiding health professionals in diagnosing hair conditions accurately.

This study developed a convolutional neural network model for non-invasive diagnosis of androgenetic alopecia using dermoscopic images, demonstrating potential accuracy and scalability while highlighting the importance of model interpretability for clinical use.

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

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 used a deep learning approach that successfully predicts alopecia, psoriasis, and folliculitis with a 2D convolutional neural network, achieving a training accuracy of 96.2% and validation accuracy of 91.1%.

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 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.

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.

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.

The researchers developed a comprehensive human skin cell atlas using data from various studies and established a consensus nomenclature for normal human skin in this project, which also includes a deep learning-based method for more effective reference mapping of new cells.

This study developed a deep learning-based method that identified 3,620,516 potential drug-disease associations, suggesting a promising tool for large-scale virtual screening in drug research.

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 study utilized the Random Forest Algorithm to create a machine learning model aimed at accurately predicting hair loss by considering complex datasets involving genetic, hormonal, lifestyle, and environmental factors, but specific outcomes were not reported.

This study found that the DNN-DTIs prediction model achieved high accuracy in predicting drug-target interactions, suggesting its potential application in drug repositioning and the discovery of new uses for existing drugs.

In this study, the integration of obesity-related features and machine learning techniques significantly enhanced cardiovascular disease detection, with the XGBoost classifier achieving a 74% accuracy rate and improved metrics compared to other models.