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This study explored the role of AI technologies in plastic and reconstructive surgery, highlighting their potential across preoperative, intraoperative, and postoperative stages, while also identifying challenges like data privacy and regulatory issues that must be addressed for successful implementation.

This systematic review provides an in-depth evaluation of non-invasive imaging and biophysical methods for diagnosing and monitoring vitiligo, emphasizing the need for studies with larger sample sizes to verify their use in clinical practice and research.

This review discusses how recent advancements in computational and lab-based methods have enhanced peptide drug discovery, noting the efficiency and cost benefits over traditional approaches and the potential for targeting difficult protein interactions, including protein degradation using proteolysis-targeting chimeras.

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.

This study found that treating reconstructed human epidermis with beta-lipohydroxy salicylic acid increased tight junction remnants in the stratum corneum, potentially affecting skin cohesion and desquamation.

This study demonstrates that modeling clone size independently of time offers analytical advantages for understanding cell distribution and mutation frequency in epithelial progenitors, extending applications beyond neutral models.

This study reported that the Computational Analysis of Novel Drug Opportunities platform effectively uses integrated biological data, including side effects and pathways, to generate potential drug candidates for colon cancer and migraine disorders.

This perspective highlights how advancements in single-cell sequencing and digital pathology can help understand the complex mechanisms of immune responses, fibrosis, and tissue remodeling related to medical implants, aiming to address the challenges of implant-related tissue reactions reported in this study.

This study found that Mueller Matrix microscopy can accurately identify, detect, and evaluate hair follicles in mouse skin tissue, suggesting its potential for skin structure research and dermatological applications.

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 study found that the peptides RMYYY and VMYMI displayed stronger binding energy and more frequent interactions with HPGDS compared to the native inhibitor, suggesting potential as future therapeutic drugs.

This study reports that a robotic and AI-based system successfully analyzes FUE hair transplant procedures, aiding surgeons in planning and assessing operation success through detailed pre-op and post-op evaluations.

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 survey investigated how well an AI chatbot could generate dermoscopic language reports for dermatologists and found that participants were equally satisfied with its responses across scenarios, despite lower performance in diagnosing SCC and inflammatory dermatoses.

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.

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 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 review examines physical methods like electron paramagnetic resonance spectroscopy, Raman spectroscopy, and differential scanning calorimetry, highlighting their complementary capabilities in analyzing biomolecular structures and mycobacterial interactions with reactive oxygen species and antioxidants.

This article discusses the increasing trend of retargeting existing drugs for new indications and emphasizes the need for additional support in drug development, without reporting specific new results.

This study highlights that variations in clinical outcome reporting can undermine the validation of NTCP models for RIA, stressing the need for a standardized and objective scoring system.

This study reported that a new deep learning algorithm using Mask R-CNN improved hair follicle classification accuracy by 4 to 15%, suggesting potential clinical application for enhanced hair loss diagnosis.

In this study, a novel GAN-based image translation method focusing on regions of interest showed improved predictive performance for post-hair transplant images compared to existing methods, using an ensemble approach to enhance robustness and detection accuracy.

This review highlights that synthetic biodegradable polymers like PVA, PCL, PLA, PLGA, PU, and PEO/PEG can address the limitations of natural polymers in wound healing, offering improved mechanical properties and slower degradation, thereby enhancing their application as wound dressings.

This study proposes a new mathematical model, the Harmonic Mean equation, for precisely quantifying nuclear pleomorphism in breast cancer grading, showing high performance with accuracy, recall, specificity, precision, and F1-score metrics.

This review discusses the growing role of colorimetric sensors in food safety, highlighting their potential to enhance detection accuracy and manageability, while also noting challenges such as environmental interference and the need for further industrial validation.

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.

This review highlights that microRNAs (miRNAs) play crucial roles in hair follicle development and cycling in cashmere goats, detailing recent advances in understanding their regulatory functions and potential applications in improving cashmere fiber quality and diagnosing hair disorders.

This review highlights that gut dysbiosis and bacterial extracellular vesicles are key factors in PCOS pathophysiology, and suggests AI-driven analysis of these profiles could enhance diagnostic accuracy and treatment personalization, though ethical concerns like data privacy and bias must be considered.

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