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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.

AI and advanced technologies are improving medical diagnostics and treatments.

In this study, researchers evaluated various machine learning models for predicting type 2 diabetes risk, finding that Random Forest and K-NN models performed best in terms of precision, recall, accuracy, and other metrics using common symptoms as features.

In this study, a child's diagnosis of Silver-Russell syndrome was confirmed through phenotype data, genetic testing, and the exclusion of other developmental conditions, revealing a need for a multidisciplinary care approach.

This study observed that COVID-19 infection in cancer patients may worsen lung damage, leading to over a 50% reduction in lung volume and increased lung density due to scar tissue formation.

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 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 review examines the current applications and potential of artificial intelligence in healthcare, highlighting its role in improving prevention, diagnosis, and treatment across multiple major disease areas, but reports no new experimental findings.

In this study, the proposed MSF-VMDNet, combining dual encoder networks with a multi-frequency domain mechanism, significantly outperformed existing methods in segmenting skin cancer tissues from histological slide images, achieving high accuracy with an MIoU of 95.37% and a Dice coefficient of 95.11%.

This review discusses recent progress in PENG-based sensors for non-invasive medical diagnosis and treatment but reports no new experimental results.

In this retrospective cohort study, researchers found that exposure to 5-alpha reductase inhibitors and spironolactone in female alopecia patients was not linked to an increased risk of developing malignant or benign gynecologic tumors when compared to minoxidil use.

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 discusses recent advancements in triboelectric nanogenerator-based electronics for clinical applications and reports no new experimental results, focusing on customization for chronic disease diagnosis and long-term treatment.

This article contains the late-breaking abstracts from the 39th Annual Conference of the American Society for Laser Medicine and Surgery and does not present new research results.

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 review discusses Barrett's esophagus, its risk factors, diagnostic methods, and treatment options, emphasizing its potential for malignant degeneration and the importance of personalized treatment based on patient factors.

This study introduced a novel framework called SL-HyDE that significantly improved zero-shot dense retrieval accuracy in medical information retrieval without relying on labeled data.

This review discusses the current knowledge on using special and immunohistochemical stains for diagnosing hair disorders, concluding that no stains are recommended for routine use in hair pathology.

This study found that patients with tropical chronic pancreatitis exhibited significantly reduced plasma amino acid levels and antioxidant capacity, with folate deficiency identified as a key factor in hyperhomocysteinemia.

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 review examines the impact of biotin, vitamin B12, and zinc on male reproduction, emphasizing their role in spermatogenic failure, but reports no new clinical results.

Improving end-of-life care at home requires better coordination, communication, and support.

This article explores how natural and man-made factors shape architecture and environments for human activity in emergency situations, focusing on the unique conditions of the Republic of Kazakhstan. Results are not reported.

This study found that cell-free RNA, particularly DNAJB9, shows potential as a biomarker for diagnosing and prognosing female androgenetic alopecia using a machine learning model.

This review discusses the potential uses of inorganic nanomaterials for imaging and detecting brain diseases, but reports no new experimental results; it calls for improvements in diagnosis precision and imaging accuracy.

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 article highlights that early and high-dose corticosteroid therapy, along with immunosuppressive agents, is crucial for managing Vogt-Koyanagi-Harada disease, and emerging biological therapies may benefit refractory cases.

This chapter reviews the challenges and future prospects of using nanotechnology and phytochemistry in developing safe, effective, and precision-based therapies for Polycystic Ovary Syndrome, without reporting new clinical findings.

This study introduced a novel deep clustering approach for melanoma detection from dermoscopic images, demonstrating improved performance over existing methods by mitigating class imbalance issues using a center-oriented margin-free triplet loss.

This study reported that AutoML effectively modeled itching and pain development, as well as app use, in patients with chronic eczema or psoriasis using a smartphone monitoring app, revealing that factors like BMI, age, and disease activity significantly influenced app engagement.