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This review examines the current state of automated vitiligo detection systems, noting a lack of large, diverse datasets and consistent imaging conditions, while comparing traditional and modern machine learning approaches to improve reliability and applicability.

This study found that by using an automated system to analyze skin histology, 108 structural features were significantly affected by age, and it identified four new aging biomarkers and notable sex-based differences in skin aging.

This study introduced a new high-throughput method for analyzing scalp hair morphology and found that quantifying hair form provides more accurate information than traditional classification based on racial categories, challenging the belief that cross-sectional morphology predicts hair curvature.

In this simulation study, higher cannabis exposure was associated with increased hair shedding severity and reduced follicular density, with greater effects observed in female-assigned profiles, but these associations are based on modeled data and not clinical observations.

This study utilized magnetic resonance imaging to noninvasively visualize and differentiate skin structures in rat skin, finding a significant correlation between MRI data and histological areas.

This study applied semantic text-mining to identify phenotypes linked to over 6,000 diseases, demonstrating that these phenotypes can accurately identify known disease-associated genes, creating a human disease network based on phenotypic similarity.

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.

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

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.

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.

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 introduces AlopeciaDet, a novel feature fusion technique, using camera images to detect Alopecia Areata with 99.45% accuracy, outperforming existing methods by leveraging CRSHOG and ResNet-50 features.

This study reviews the transformative role of artificial intelligence in biomaterial design, highlighting its ability to reduce costs through virtual screening, enhance material performance, and predict biological interactions to advance personalized and precision medicine.

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 found that their newly developed ADRL framework significantly improved the accuracy of scalp tissue layer segmentation in HR-MR images compared to existing methods.

This study introduces Cell Journey, a new platform for visualizing RNA velocity in 3D, which aims to better capture complex cellular transitions in single-cell datasets compared to current 2D methods.

In this study, a Monte Carlo-generated synthetic cohort analysis found that higher simulated cannabis exposure was linked to increased self-assessed hair loss severity and reduced follicular density, suggesting potential connections worth exploring in further clinical research.

This study demonstrates that spatially resolved IR and Raman imaging can effectively reveal the molecular structure and composition of untreated human hair, providing insights into its chemistry and structural integrity.

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.

Gender affects hair and scalp characteristics, with differences in hormone responses, graying patterns, and trace metals.

This article describes current advancements and future possibilities in skin imaging technology, teledermatology, and AI in dermatology, without reporting new research results.

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 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.

In this paper, Chemistry42—a software integrating AI with computational and medicinal chemistry—demonstrated efficiency in designing novel molecular structures targeting DDR1 and CDK20, with properties validated in both in vitro and in vivo studies.

In this study, researchers developed a machine learning model that accurately diagnosed scalp conditions like fine dandruff and perifollicular erythema with 75% and 82% accuracy, respectively, and created a user-friendly web platform for scalp health self-assessment, which achieved high user satisfaction.

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 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.

Dutasteride and finasteride do not increase mood disorder risk in men with hair loss.

This study suggests moving away from using rigid racial categorizations and outdated typologies in favor of analyzing detailed trait patterns, providing a foundation for future research on human variation and hair traits in relation to population affinity.

In this study, researchers successfully constructed a high-precision three-dimensional model of human skin dermis, revealing a detailed analysis of dermal porosity and pore diameter distribution, which can aid the development of biomimetic tissue-engineered skin.