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In this retrospective cohort study, researchers found that exposure to 5-alpha reductase inhibitors and spironolactone in female alopecia patients was not linked to an increased risk of developing malignant or benign gynecologic tumors when compared to minoxidil use.

This review highlights new perspectives in genomics and epigenomics for skin rejuvenation, comparing innovative strategies like senolytics and DNA repair modulators with classical treatments, and emphasizing the importance of tailoring therapies using individual genomic profiles for personalized anti-ageing approaches.

This study discusses the potential of advanced imaging technologies in dermatology to improve diagnostic accuracy and reduce the need for invasive procedures like biopsies, while noting significant barriers in adoption and accessibility in India due to costs and infrastructure constraints.

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 highlights emerging regenerative strategies, such as stem cell-derived therapies and machine learning tools, that may advance hair loss treatment beyond traditional methods by promoting follicle regeneration and offering personalized care.

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 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 found that self-supervised pretraining significantly improves accuracy in medical image classifiers for dermatology and chest X-ray tasks, outperforming supervised baselines and showing robustness to distribution shifts with limited labeled data.

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 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 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%.

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 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, 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 introduced a multitask learning method to identify shortcut learning in clinical ML systems, revealing it's not always responsible for unfairness and emphasizing the necessity of comprehensive fairness approaches in medical AI.

This study used machine learning to identify molecular predictors of drug response in alopecia areata, suggesting a tool for predicting treatment efficacy based on gene signatures.

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 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.

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

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 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.

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 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.

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.

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 research explores the impact of advanced technologies, such as machine learning and robotics, on cardiothoracic surgery, noting that innovations like artificial hearts and enhanced circulatory support systems may improve patient outcomes by aiding diagnostics, surgery planning, and postoperative care.

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 research reported that using J48 algorithms with bagging improves prediction accuracy of hair health through machine learning by analyzing factors like spatial-temporal images, gender, and age, achieving a real-time performance of 89.5%.

This article reviews drug repurposing as a strategy to overcome challenges in drug discovery, emphasizing AI and machine learning's role, but presents no new clinical results.

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.