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This study evaluated a new AI-linked imaging device for grading dandruff severity, finding high correlation with expert assessments and suggesting its potential application in other skincare areas due to its versatility and ease of use.

This review found that AI in dermatology shows high diagnostic accuracy comparable to experienced clinicians, but integration into clinical practice faces challenges requiring further research.

This review assesses the potential of smart dressings, engineered to respond to various stimuli, as advanced treatments for chronic inflammatory skin diseases by examining their activation mechanisms and proposing future developments like integrating AI and closed-loop monitoring for adaptive therapy.

This study found that the HairComb algorithm achieved high accuracy in quantifying percentage hair loss across various types of alopecia, suggesting its potential for standardized automated assessments.

This review discusses the dermatologic manifestations and management of polycystic ovary syndrome, underscoring the need for mechanism-based, personalized treatments and integrated mental health support, but reports no new clinical results.

In this study, researchers found that differences in skin condition distribution are the main source of errors when AI algorithms classify dermatological conditions from new, previously unseen sources, and proposed steps to improve their generalizability based on available information.

This study used machine learning models to analyze PCOS symptoms for early diagnosis, finding Support Vector Machine and VGG16 algorithms achieved high accuracy rates of 94.44% and 98.29% respectively.

This paper introduces an algorithm for using smart device-mounted microscopes to analyze scalp images and diagnose hair loss by extracting specific hair loss features.

This study reports that an Explainable AI-based hair health prediction system using a novel hybrid model outperformed traditional machine learning methods, achieving high accuracy in predicting key factors and providing personalized recommendations.

This review explores the skin benefits of exercise and discusses how artificial intelligence can tailor exercise programs to individual skin health needs, emphasizing the potential of AI-driven strategies in cosmetic dermatology for more personalized and adaptive skincare approaches.

This study introduced a non-invasive, three-step diagnostic algorithm for alopecia that combines clinical and trichoscopic features to accurately identify alopecia subtypes, potentially reducing reliance on invasive histopathology.

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.

In this study, the integration of artificial intelligence in medicine was discussed, highlighting its potential to enhance diagnostic processes, optimize therapies, and provide advanced patient monitoring despite challenges like data inconsistency and limited model transparency.

In this study, machine learning-based computer-aided diagnosis significantly improved accuracy in diagnosing alopecia areata compared to traditional visual methods, achieving up to 91.9% accuracy using different classifiers like CNN, SVM, and random forest models.

This study discusses the development of AI in dermatology and cosmetology, noting its use in skin cancer diagnosis and personalized cosmetics, while highlighting uncertainties about whether recent advancements represent significant medical progress or are influenced by marketing strategies.

This study outlined modern approaches for using cosmeceuticals in trichology, emphasizing evidence-based medicine, personalized care, and patient safety. It proposed the H.A.I.R. model as a universal algorithm for trichological programs and suggested new educational and practical tools for specialists in trichology and aesthetic medicine.

This study suggests that artificial intelligence could significantly enhance histology education for medical and dental students, but its implementation faces challenges like cost, accuracy, and ethical considerations.

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.

This review highlights evidence that exercise benefits skin health by enhancing collagen synthesis and reducing oxidative stress and discusses the potential of AI technology to personalize exercise programs, optimizing skin benefits based on individual needs and environmental factors.

This systematic review found that digital health interventions may improve quality of life for dermatology patients, especially through telemedicine, which often matched or exceeded in-person care. However, the evidence is limited by study variability and short follow-up periods.

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 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 highlights the growing role of artificial intelligence in vascular surgery, where AI improves diagnostic accuracy, surgical planning, and patient monitoring, ultimately enhancing clinical outcomes, shortening recovery times, and reducing healthcare costs.

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 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 source reports that clinical trials using advanced Deep Brain Stimulation systems, augmented with AI to integrate kinematic data, eye tracking, and cognitive assessments, show promise in improving diagnostic accuracy and monitoring symptoms for patients with Parkinson's disease.

This study used machine learning to identify FDA-approved drugs afatinib, neratinib, and zanubrutinib as potential KRASG12C inhibitors for resistant non-small-cell lung cancer, highlighting the potential of AI in drug repurposing.

This article highlights both the opportunities and challenges of using big data in dermatology, noting potential benefits like improved diagnostics and public health monitoring, alongside challenges such as data quality and AI training disparities.

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.

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.