research A Review on the Form and Complexity of Human–Robot Interaction in the Evolution of Autonomous Surgery
Human-robot interaction becomes simpler as robots achieve full autonomy in surgery.
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210-240 / 1000+ resultsresearch Past, present, and future of global research on artificial intelligence applications in dermatology: A bibliometric analysis
AI in dermatology is growing rapidly, showing promise in diagnosing skin conditions as accurately as dermatologists.
research AB-MTEDeep Classifier Trained with AAGAN for the Identification and Classification of Alopecia Areata
The AI model accurately classifies Alopecia Areata with 96.94% accuracy.
research Anti-Alopecia Activity of Alkaloids Group from Noni Fruit against Dihydrotestosterone-Induced Male Rabbits and Its Molecular Mechanism: In Vivo and In Silico Studies
Noni fruit extract, specifically the FEA-3 sub-fraction, can increase hair growth and reduce baldness in male rabbits, potentially acting like common hair loss treatments.
research Poster Abstracts
The model and estimator can predict drug exposure in kidney transplant patients well.
research Fibroblast state switching orchestrates dermal maturation and wound healing
Fibroblast state switching is crucial for skin healing and development.
research Organoid‐Guided Precision Medicine: From Bench to Bedside
Organoid technology is improving personalized medicine by better predicting drug responses and treatments.
research Skin image analysis for detection and quantitative assessment of dermatitis, vitiligo and alopecia areata lesions: a systematic literature review
Computer vision techniques can help detect and assess skin conditions like vitiligo, alopecia areata, and dermatitis.
research Exploring machine learning strategies for single-cell transcriptomic analysis in wound healing
Machine learning and single-cell analysis improve understanding and treatment of wound healing.
research Detection of Meibomian Gland Dysfunction by in vivo Confocal Microscopy Based on Deep Convolutional Neural Network
The model can effectively help diagnose meibomian gland dysfunction automatically.
research Leveraging deep neural networks to uncover unprecedented levels of precision in the diagnosis of hair and scalp disorders
A new AI model diagnoses hair and scalp disorders with 92% accuracy, better than previous models.
research Perceptions and utilization of telemedicine for hair loss treatment: A survey-based analysis of Korean consumers
Many are open to telemedicine for hair loss if combined with in-person visits and better technology.
research Artificial Intelligence-Based Personalization of Treatment Regimen for Hair Loss: A 6-Month Clinical Trial.
AI-personalized hair loss treatments improved hair growth and scalp health without side effects.
research Development and Prospective Applications of 3D Membranes as a Sensor for Monitoring and Inducing Tissue Regeneration
3D-printed membranes with smart sensors can greatly improve tissue healing and have many medical applications.
research Drug repositioning using in silico compound profiling
Using computers to analyze drugs can find new uses for them, but actual experiments are needed to confirm these uses.
research Design and fabrication of customizable microneedles enabled by 3D printing for biomedical applications
3D-printed microneedles improve drug delivery and diagnostics but face scalability and regulatory challenges.
research Analyse trichoskopischer Bilder mit tiefen neuronalen Netzen zur Diagnose und Aktivitätsbewertung von Alopecia areata – eine retrospektive Studie
Deep-learning models can effectively diagnose and assess Alopecia areata using scalp images.
research Multilayered Gel-Spotting Device for In Vitro Reconstruction of Hair Follicle-like Microstructure
The new method can create hair follicle-like structures but not complete hair with roots and shafts, needing more improvement.
research Order from disorder: Self-organization in mammalian hair patterning
Hair patterns in mice are controlled by both a global system dependent on Fz6 and a local self-organizing system.
research Foundations of Bayesian Networks
Bayesian networks are tools for modeling variables' probabilistic relationships, which can efficiently represent complex probabilities and help in making inferences.
research Experimental Evidence in Hair Restoration Procedures: Plucked Hair Survival and Growth Rate.
Plucked hair follicles grow faster than conventional ones, making them a potentially better option for hair transplants.
research Computerized morphometry and three-dimensional image reconstruction in the evaluation of scalp biopsy from patients with non-cicatricial alopecias
The study suggests computer-assisted analysis of scalp biopsies could improve hair loss diagnosis but needs more validation.
research Confirmatory factor analysis of the causal illness attribution scale in Chinese patients with multiple somatic symptoms
The two-factor model fits better for Chinese patients' understanding of illness causes than the original four-factor model.
research Towards Fairer Health Recommendations: finding informative unbiased samples via Word Sense Disambiguation
Fine-tuned BERT models are better than LLMs for detecting bias in medical data.
research A DEEP LEARNING APPROACH FOR DIAGNOSIS OF COVID-19 INFECTION AND ITS RELATED FACTORS: A POPULATION-BASED STUDY
The random forest model effectively helps diagnose COVID-19 using key factors like age and symptoms.
research A Hybrid Transfer Learning Approach Using Obesity Data for Predicting Cardiovascular Diseases Incorporating Lifestyle Factors
Adding obesity data to machine learning models improves heart disease prediction accuracy.
research Computing Your Ideal Haircut Routine
The ideal haircut routine can be determined using a model based on hair growth and regular haircuts.
research Quantifying Alopecia Areata via Texture Analysis to Automate the SALT Score Computation
Researchers created a fast, accurate computer program to measure hair loss in alopecia areata patients.
research Weakly supervised learning of biomedical information extraction from curated data
The method can effectively extract biomedical information without needing expert annotation, performing better than previous models.
research 644 Tumor cell adhesion as a risk factor for SLN metastasis and predictor of disease recurrence in primary cutaneous melanoma
Tumor cell adhesion is linked to higher risk of SLN metastasis and melanoma recurrence, and a model including these factors predicts these outcomes better than one with just clinical data.