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This study suggests that histological features of primary melanoma can partially predict lymph node metastasis using AI, achieving a best prediction AUROC of 0.65.

In this research, experienced clinicians noted inconsistency in the reproducibility of the commonly used Hamilton-Ludwig scales for assessing pattern hair loss severity in photographic assessments, leading to the proposal of a new 5-point scale for staging female hair loss.

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.

This study found that a new FAW-FS algorithm improved recognition of depression in patients with androgenic alopecia, and comprehensive psychological interventions positively impacted their rehabilitation outcomes.

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.

This study emphasizes the need for personalized hair care strategies based on in-depth diagnostics, taking into account scalp type, environmental factors, and the microbiome, suggesting that this holistic approach could improve hair condition.

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 found that artificial intelligence assistance improved diagnostic accuracy in dermatologic cases for primary care physicians and nurse practitioners, with higher agreement rates with reference diagnoses observed.

A machine learning model can predict scarring in lichen planopilaris using factors like vitamin D levels and diagnostic delay.

This review examines nanozymes as promising tools for cancer diagnosis and therapy due to their enzyme-mimicking catalytic properties and highlights their challenges, including biosafety and clinical translation, alongside potential improvements through biomimetic designs, particularly utilizing exosome-based strategies for targeted delivery to tumors.

This study reports that using machine learning models, particularly XGBoost and Random Forest, can accurately predict PCOS phenotypes based on non-invasive data, with cycle length as the most significant predictor.

This study assessed the potential for Ocean Thermal Energy Conversion power in Fiji, finding higher power output and efficiency during summer due to greater temperature differences between surface and deep sea waters.

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 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 highlights that recent advancements in diagnostics and therapies, including AI-enhanced imaging, biomarker analysis, and novel treatments like low-dose oral minoxidil and JAK inhibitors, are improving the precision and customization of hair loss management.

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.

Smart microneedles using advanced tech could improve psoriasis treatment.

This study identified distinct plasma miRNA profiles in alopecia areata that may aid in diagnosis and therapy, but further validation is needed to confirm these exploratory findings.

In this study, the integration of obesity-related features and machine learning techniques significantly enhanced cardiovascular disease detection, with the XGBoost classifier achieving a 74% accuracy rate and improved metrics compared to other models.

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 study found that a deep learning approach successfully predicted three types of hair and scalp diseases with high accuracy, despite challenges in dataset availability and image variety.

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.

This review outlines evidence-based strategies for diagnosing pediatric hypopigmented disorders and emphasizes distinguishing vitiligo from self-limiting conditions through a systematic clinical approach; no new results are reported.

This review highlights that AI-based echocardiography in cardiology can enhance diagnostic precision, automation, and therapeutic strategy development, but also presents challenges like diagnostic errors and high costs, indicating a cautious yet promising progression in its adoption.

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 study explored AI's potential in trichology services, finding it beneficial for diagnostics and treatment supervision, but noted the importance of addressing legal considerations, particularly in the Polish market.

This study highlights the importance of developing standardized diagnostic tools, outcome measures, and evidence-based interventions to improve care and long-term outcomes for children and adolescents affected by long COVID-19.

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 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 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.