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This study found that about 80% of known fullerene-binding proteins rank in the top 10% of scorers for C60 docking sites, confirming the accuracy of the predictive model used.

This study used artificial neural networks to predict hair loss by analyzing factors like gender and zinc deficiency, suggesting neural networks may effectively model hair loss prediction.

This study developed a new method for analyzing multivariate time-series data that successfully predicts website competition dynamics and outperforms conventional methods in identifying and predicting competitive structures.

This systematic review identified and evaluated the most accurate ICD code classification methods for dermatologic conditions in US-based datasets, highlighting both high-performing algorithms and areas lacking validation, thereby informing future research and dataset use.

This study found that using XGBoost with clinical and ultrasound features may provide a highly accurate, non-invasive method for diagnosing polycystic ovary syndrome, although further validation is needed to ensure robustness.

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 study found that anagen stage protein homogenates and specific epitopes from melanogenesis proteins activated CD8 T cells, suggesting alopecia areata is an anagen-specific disease.

This study aims to develop an automatic machine learning-based method using the VGG-19 model to accurately classify various hair and scalp diseases.

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.

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.

This research explored the use of machine learning and deep learning methods to accurately identify alopecia areata in humans by analyzing facial images and demonstrated the potential of these techniques for medical, security, and commercial applications.

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 guideline reviews the diagnosis of androgenetic alopecia and offers expert consensus recommendations tailored to males, females, and adolescents, but reports no new clinical results.

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 study found that securinine and ajmaline significantly inhibited hepatocellular carcinoma cell viability and induced apoptosis, with securinine showing lower toxicity to normal liver cells.

In this study, treatment of unwanted hair with 755 nm alexandrite laser in 14 patients showed a decrease in excess hair at the armpit and thigh areas, with a 75% accuracy rate in hair density estimation using Monte Carlo modeling.

This study evaluated treatments for frontal fibrosing alopecia and found hydroxychloroquine and 5a-reductase inhibitors to be most effective, with 70-72% of patients experiencing stabilization or improvement, but limitations in treatment consistency and assessment methods remain.

This article discusses the genetic polymorphisms affecting the antioxidant system and suggests that personalized detoxification plans and nutrition may be beneficial for patients with chronic diseases, but it reports no new clinical findings.

The researchers reported that the novel MBTPS2 variant p.Glu172Asp found in a male proband is likely pathogenic, consistent with osteogenesis imperfecta symptoms and molecular signatures, including disrupted fatty acid metabolism and collagen production.

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 used machine learning to identify molecular predictors of drug response in alopecia areata, suggesting a tool for predicting treatment efficacy based on gene signatures.

This study found that specific DNA variants in the TCHH, WNT10A, and FRAS1 genes are associated with predicting straight hair in Europeans, showing high sensitivity but low specificity, especially using a neural networks approach.

This study identified several compounds from traditional Chinese medicines that may inhibit prostaglandin D2 synthase, potentially providing promising candidates for treating androgenic alopecia with minimal adverse skin reactions.

This study found that the Random Forest machine learning algorithm achieved the highest accuracy, 96%, in diagnosing Polycystic Ovarian Syndrome using patients' clinical data.

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 demonstrated that a CNN-based model using VGG-16 architecture achieved a 94.5% accuracy in classifying ten types of hair diseases, implying a promising tool for aiding health professionals in diagnosing hair conditions accurately.

This review suggests a shift toward genetically informed treatments for male pattern hair loss by integrating genetic insights and pharmacogenetic markers into therapeutic decision-making.

This study conducted molecular docking and ADME property analysis on 144 newly designed isatin analogs, suggesting they exhibit lead-like properties for targeting EGFR enzymes.

This study reported that a subcutaneous injection formulation using PLGA microspheres to release finasteride stably achieved sustained monthly drug delivery without burst release in beagle dogs, with a dose of 16.8 mg identified as optimal for potential first-in-human trials.

This review analyzes the research on production delivery strategies using n-vehicle, highlighting recent developments and suggesting areas for future study, but it reports no new experimental findings.