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The researchers reported that in patients with contact dermatitis, certain immune and skin barrier biomarkers, such as Th-cell mediated markers and cholesterol derivatives, may help distinguish between irritant and allergic subtypes, suggesting potential for improved diagnosis through integrated analysis.

This review explores recent advancements in skin tissue engineering using 3D bioprinting, discussing current methods, bioink formulations, and outlining both achievements and limitations without presenting new clinical results.

Smart microneedles using advanced tech could improve psoriasis treatment.

This study suggests that estimating autism likelihood as early as one month after birth may enable more precise early intervention for children with developmental support needs, potentially improving diagnosis, workflows, and reducing service wait times.

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

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.

In this study, researchers developed a novel computational framework using deep reinforcement learning to identify strategies for cellular reprogramming in gene regulatory networks, showing its effectiveness in a model of immune response against infection.

This review discusses 'vibe coding', a new approach that allows clinicians with minimal coding skills to utilize machine learning tools for medical research by using natural language directives to generate and refine code through AI-driven platforms.

This study bibliometrically evaluated 5741 articles on PRP from 1980 to 2024, highlighting Türkiye's contribution and identifying prominent research areas such as orthopedics and wound healing.

In this study, the analysis of PCOS subreddit data revealed that lifestyle changes and supplements were perceived positively for managing PCOS symptoms, while contraceptives were often linked with negative experiences, except when they included anti-androgenic progestins.

This study analyzed gene expression data to identify key genes involved in severe forms of alopecia areata, discovering four immune monitoring genes (LGR5, SHISA2, HOXC13, S100A3) with potential for early diagnosis and better understanding of the disease's biological mechanisms.

This study found that a microneedle patch using manganese thiophosphite showed greater hair regrowth potential compared to minoxidil, even with less frequent application, for treating androgenetic alopecia.

This study developed a deep-learning model using unannotated dermatology images from online forums, achieving 49.64% accuracy in classifying 22 skin diseases and 61.76% accuracy in detecting monkeypox, highlighting the potential of these images for skin disease diagnostics in China.

This study found that machine learning techniques, such as Random Forest, SVMs, and KNN, can significantly improve the early detection and determination of hair loss, potentially transforming treatment with more accurate and personalized approaches compared to traditional methods.

This review discusses the increasing role of machine learning in drug repurposing, demonstrating how algorithms can reveal new therapeutic uses for existing drugs and predict side effects, with potential benefits for precision medicine, while also addressing ethical and privacy concerns.

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.

Machine learning is effective in predicting gene functions and their relationships with diseases.

This review reports that while deep learning shows promise in diagnosing scalp disorders from images, challenges remain with data quality and model interpretability, suggesting that integrating explainable AI techniques is crucial for building trust and facilitating clinical adoption.

This study developed a deep learning framework and quantitative model that accurately predict basic and specific classification in male androgenetic alopecia by analyzing trichoscopic images.

This study proposed and tested an Ensemble Pre-Learned Deep Learning and Optimized Long Short-Term Memory (EPL-OLSTM) model for classifying Alopecia Areata, achieving a 93.1% accuracy in differentiating healthy from varying severity levels of AA scalp hair using specific datasets.

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.

In this study, researchers identified a causal link between Epstein-Barr virus infection and clear cell renal cell carcinoma, highlighting GBP1 as a key target and suggesting finasteride as a potential inhibitor, offering a new direction for treatment strategies.

This study proposes a deep learning model using CNN with VGG16, VGG19, and MobileNetV2 architectures, achieving high accuracy in classifying scalp diseases from images, potentially facilitating diagnosis and treatment via mobile devices.

The LTF gene may help predict and manage nonspecific orbital inflammation.

This study introduces the Hair Cell Analysis Toolbox (HCAT), a machine-learning software that automates the analysis of cochlear hair cells, enabling unbiased and comprehensive imaging data interpretation.

This study found that while rosacea management is the most frequently discussed topic on social media forums related to disease, emotional expression is also a significantly prevalent activity among users.

This study evaluated different convolutional neural network architectures for diagnosing scalp and hair diseases, and found that VGG16 and VGG19 consistently outperformed other models in accuracy, demonstrating their effectiveness and reliability in this medical application.

This review examines the current state of automated vitiligo detection systems, noting a lack of large, diverse datasets and consistent imaging conditions, while comparing traditional and modern machine learning approaches to improve reliability and applicability.

This scoping review describes the current use of artificial intelligence in alopecia research, highlighting AI's evolution from diagnostic to prognostic applications in dermatology and identifying gaps in multimodal integration and fairness across demographics.