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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 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 developed a leakage-resistant machine learning framework for classifying hair loss types, emphasizing transparency through explainable AI. Among tested models, Extreme Gradient Boosting excelled, achieving high accuracy and stability on both cross-validation and holdout datasets.

In this study, a deep neural network model called regX was developed to prioritize driver regulators for cell state transitions by incorporating gene-level regulation and interactions, showing potential therapeutic targets in type 2 diabetes and hair follicle development when applied to single-cell multi-omics data.

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 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 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 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.

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 narrative review found that while AI-based tools in dermatology, particularly for hair disorder assessment, have potential to enhance clinical practice by improving objectivity and personalization, they currently serve mainly a complementary role and face challenges like methodological limitations and data bias.

In this review, researchers summarize key factors in constructing skin organoids, including cell source and assembly methods, and emphasize advances such as air-liquid interface culture for improving tissue development, aiming to guide standardized protocols and future clinical applications.

In this study, researchers identified two person-centered sexual function profiles among women, linked to physical and psychological factors, with PCOS showing greater, though not significant, presence in the dysfunction profile. The dysfunction was associated with higher adiposity and body-image distress.

This review found that vesicle-based therapies, including mammalian MSC-derived and plant-derived vesicles, consistently promote wound healing in preclinical models of thermal injury and may offer a safer alternative to live cell transplants.

In this study, baseline neutrophil-to-lymphocyte ratio (NLR) was associated with predicting early trichoscopic response in patients undergoing PRP-based treatment for non-scarring alopecia.

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 created transgenic mouse models with the LMNA gene mutation common in Hutchinson-Gilford progeria syndrome, revealing skin and teeth abnormalities related to transgene expression levels.

At the 21st Royan International Stem Cell Congress, researchers highlighted the growing integration in regenerative medicine, emphasizing advances in pluripotency, AI applications, and bioengineering for the development of accessible stem cell therapies.

This research introduces WomenCare, a web-based system using a machine learning model to predict PCOD risk by evaluating factors like age, BMI, and lifestyle habits; it aims to help women monitor their health but is not a substitute for a professional diagnosis.

This study reviews AI's role in aesthetic medicine, noting it enhances diagnostic accuracy and personalized treatment planning, but faces challenges like ethical concerns, algorithmic biases, and regulatory issues that need addressing for successful integration.

This article examines challenges in the pharmaceutical supply chain during pandemics, highlighting issues like raw material sourcing and distribution delays, and suggests enhancements such as AI, blockchain, and public-private alliances to ensure stability and resilience.

This article outlines the rationale and design of the Rotterdam Study, summarizes its major findings, and updates its objectives and methods; it reports no new research results.

This review examines the reliance on formalized and automated protocols in fields like aviation and medicine, suggesting that such approaches may hinder the human capability of mètis needed to handle emergencies and dynamic ambiguities; it reports no new results.

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 review guides readers on how to critically evaluate multiple treatment comparison meta-analyses, offering insights for interpreting and communicating these studies but reporting no new results.

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 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 discusses existing forensic DNA phenotyping panels for biogeographical ancestry and externally visible characteristics and highlights major technical limitations, including terminology issues, genetic knowledge gaps, and technological debates; it reports no new results.

This review describes the clinical, trichoscopic, and histopathological features of hair disorders linked to autoimmune diseases and reports no new clinical results.

This article reviews the design and objectives of the Rotterdam Study, as well as summarizes major findings, without reporting new results.

This review highlights recent advances in ultrasound-based radiomics and radiogenomics for ovarian cancer, suggesting these techniques improve diagnostic accuracy and patient-specific treatment strategies, despite ongoing challenges with standardization and model interpretability.