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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.

In this study, the researchers reported that patient impacts and symptoms of hidradenitis suppurativa, as assessed by HSIA and HSSA measures, are associated with clinical characteristics such as the number of abscesses and inflammatory nodules.

This study observed that using the Fisher score feature selection approach with capsule network models led to a promising 94% accuracy in diabetes detection, indicating its potential as a diagnostic tool.

Human hair structure varies by ethnicity, and certain treatments can improve hair condition and appearance.

Among families with pediatric brain disease, this study identified over 200 novel genetic causes, revealing potential treatment points using drug repurposing or nutritional supplementation.

In this rare case report, the researchers described a 66-year-old African woman with lipedematous alopecia, highlighting its clinical features, the significance of palpation in examinations, and the uncertainty of it being a distinct disease entity or a combination of two conditions.

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.

This chapter reviews the hair follicle's role in drug and chemical penetration through the skin but does not present new experimental findings.

Twisting hair weakens it, but strength can be recovered at low twist levels.

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 reviews methods to ensure the reliability of machine learning models in medical imaging, focusing on bias detection, data drift assessment, and accuracy estimation without ground truth labels to enhance integration into clinical settings.

This study found that their proposed method for facial image restoration using Denoising Diffusion Probabilistic Models produced higher-quality results compared to traditional methods, particularly improving face recognition accuracy with different types of masks.

In this study, the authors found that syntax-based neural networks performed comparably to pre-trained Transformers on tasks involving definitely unseen sentences, suggesting they are a more transparent and parameter-efficient alternative for certain Natural Language Processing applications.

This review summarizes late-breaking abstracts from the American Society for Laser Medicine and Surgery, 2018, and reports no new clinical findings.

The document concludes that ideal facial proportions are guided by specific measurements and symmetry to enhance beauty, but individual characteristics must be considered.

This study found that using deep plane fixation during scalp surgeries significantly reduced closure tension and allowed for increased tissue excision compared to standard techniques without fixation.

This study explores the complex mechanisms of skin aging, including cellular senescence and disrupted communication, and highlights rejuvenation strategies like gene expression rewiring and microbiome modulation, offering potential frameworks for regenerative therapies and precise interventions in skin and systemic aging.

In this study, the proposed MSF-VMDNet, combining dual encoder networks with a multi-frequency domain mechanism, significantly outperformed existing methods in segmenting skin cancer tissues from histological slide images, achieving high accuracy with an MIoU of 95.37% and a Dice coefficient of 95.11%.

This review discusses the integration of Artificial Intelligence in non-surgical cosmetic procedures, highlighting its potential to improve personalized aesthetic care and operational efficiency while addressing ethical and regulatory challenges; it presents no new clinical findings.

In this study, lower dihydrotestosterone and higher testosterone levels were linked to increased brain activation during response inhibition, although no behavioral changes were detected.

This study developed a model for detecting sparse hair clusters using enhanced object detection neural networks and medical images, which accurately identifies and counts sparse hair clusters with greater accuracy and efficiency than existing methods.

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 paper discusses the use of Digital Volumetric Imaging with fluorescent dyes to visualize and compare the interior structures of six different virgin hair types, reporting no new clinical results.

A new method helps understand hair shine and various products improve hair care.

This review explores the potential of herbal therapies and modern delivery methods for managing premature hair greying, highlighting promising plant-based treatments and emphasizing the need for clinical trials to validate their effectiveness.

This study identified a four-gene loop as a non-invasive biomarker that selectively activates in alopecia areata, providing a precise target for JAK inhibitor treatments.

This paper critically evaluates the role of AI in cosmetic surgery, highlighting its potential to enhance precision, tailor treatment, and improve patient outcomes, while also addressing ethical, legal, and regulatory challenges that complicate its integration into clinical practice.

This review highlights that microscopic analysis of human scalp hair remains crucial in forensic science for crime scene investigations, despite limitations such as subjectivity and potential misidentification, and notes that integrating DNA analysis and digital imaging could enhance accuracy.

This study demonstrates that an AI-based grading framework using a novel area ratio metric improves the accuracy and consistency of male pattern hair loss classification, especially in advanced grades, compared to traditional methods.