research Detecting shortcut learning for fair medical AI using shortcut testing
The ShorT method can detect and help reduce bias in medical AI by identifying shortcut learning.
Search
for
Sort by
Research
150-180 / 1000+ results 
research Hair Disease Classification Using Convolutional Neural Network (CNN) Algorithm with VGG-16 Architecture
The system can accurately classify hair diseases with 94.5% accuracy using a CNN.
research Euphemism as a core feature of patientese: A comparative study between English and French
People use different types of euphemisms for medical terms in English and French, which can cause confusion for non-native speaking healthcare professionals. Also, medical TV shows and the internet might make these terms more technical over time.
research How good is artificial intelligence (AI) at solving hairy problems? A review of AI applications in hair restoration and hair disorders
AI is effective in diagnosing and treating hair disorders, including detecting hair loss and scalp conditions with high accuracy, but it should supplement, not replace, doctor-patient interactions.
research Quantitative analysis and development of alopecia areata classification frameworks
A new algorithm effectively classifies Alopecia Areata, aiding early detection and treatment.
research Deep Learning Approaches for Hair Disease Classification: A Comparative Analysis of MobileNetV2 and VGG19 Architectures
VGG19 is more accurate, but MobileNetV2 is faster and uses fewer resources.
research DETECTION OF HAIR FALL AND SCALP DISORDERS THROUGH ML AND IMAGE PROCESSING
AI can effectively detect hair and scalp disorders from images.
research HairLossMultinet: A Multi Scale Feature Fusion Method Using Deep Learning Approach
HairLossMultinet accurately classifies hair damage with 98% accuracy but needs a more diverse dataset for broader use.
research Ensemble of pre-learned deep learning model and an optimized LSTM for Alopecia Areata classification
The new model improves Alopecia Areata classification accuracy to 93.1%.
research KRDQN: An Interpretable Prediction Framework for Adverse Drug Reactions via Knowledge–Graph Reinforced Deep Q-Learning
KRDQN effectively predicts adverse drug reactions with high accuracy and clear explanations.
research Scalp disease analysis using deep learning models
Deep learning models can analyze scalp diseases effectively.
research Deep Learning-based Trichoscopic Image Analysis and Quantitative Model for Predicting Basic and Specific Classification in Male Androgenetic Alopecia
A deep learning model accurately predicts male hair loss types using scalp images.
research Rapid Screening of Anticoagulation Compounds for Biological Target-Associated Adverse Effects Using a Deep-Learning Framework in the Management of Atrial Fibrillation
The framework helps predict adverse effects of blood thinners, improving drug selection for atrial fibrillation.
research 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 Optimized VGG19 Architecture for Precise and Efficient Multi-Class Hair Disease Classification
The optimized VGG19 model accurately classifies hair diseases with 98.64% accuracy.
research PandaOmics: An AI-Driven Platform for Therapeutic Target and Biomarker Discovery
PandaOmics uses AI to find new disease treatment targets and biomarkers.
research Knowledge Mapping of Drug Repositioning’s Theme and Development
Drug repositioning can save time and money but needs more support.
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 Big Self-Supervised Models Advance Medical Image Classification
Self-supervised learning improves medical image classification accuracy.
research Evaluation of Automated Measurement of Hair Density Using Deep Neural Networks
Deep learning can accurately automate hair density measurement, with YOLOv4 performing best.
research THE POTENTIAL OF AI TO ENHANCE MEDICAL EDUCATION: OPPORTUNITIES, CHALLENGES, AND ETHICAL CONSIDERATIONS
AI can greatly improve medical education by personalizing learning and enhancing skills, but challenges like cost, training, and ethics need addressing.
research JAK-centric explainable few-shot gene-expression diagnosis framework for alopecia via MultiPLIER priors and relation-style set-to-set comparison
A new method helps diagnose alopecia areata using specific gene markers and could guide targeted treatments.
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 Characterization of the role of Facebook groups for patients who use scalp cooling therapy: a survey study
Facebook groups help patients using scalp cooling therapy by offering support, advice, and product recommendations.
research AutoMIR: Effective Zero-Shot Medical Information Retrieval without Relevance Labels
SL-HyDE improves medical information retrieval accuracy without needing labeled data.
research Proceedings of the III International Scientific and Practical Conference
The conference emphasized innovative solutions for global challenges, including disaster architecture, education, health, and economic impacts.
research Identification of Various Deficiencies Using Data Mining Techniques - A Survey
Data mining helps identify and address nutrition deficiencies affecting health.
research Grand challenges in dermatologic drug discovery: four priorities to transform skin disease treatment
Transforming skin disease treatment requires new strategies, better drug models, and patient-focused research.
research Deep Hair Phenomics: Implications in Endocrinology, Development, and Aging
A new tool can analyze hair to detect changes due to hormones, genetics, and aging.
research Hair Follicle Classification and Hair Loss Severity Estimation Using Mask R-CNN
An intelligent system can classify hair follicles and measure hair loss severity with reasonable accuracy.