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In this study, researchers proposed a deep learning approach combining genetic, hormonal, scalp health, and lifestyle data to predict hair loss, employing CNNs for image analysis and RNNs for modeling data over time, although specific results are not reported.

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 developed a method using Convolutional Neural Networks to detect nutritional deficiencies, such as iron, zinc, biotin, and vitamins, through high-resolution images of hair and nails, achieving an 89% accuracy rate in identifying these deficiencies.

This study introduced Neurospectrum, a framework that effectively identifies meaningful neural dynamics by encoding neural activity into latent trajectories, and reported that it outperformed traditional methods in tracking synchronization, reconstructing stimuli, and identifying fMRI biomarkers in various datasets.

In this study, the researchers developed a deep learning framework to pre-emptively screen for adverse drug effects, showing strong predictive performance, including for increased bleeding risks with edoxaban compared to other anticoagulants.

This study introduced the KRDQN predictive framework, which outperformed existing methods in predicting adverse drug reactions and provided interpretable insights into drug mechanisms, aiding pharmacovigilance and clinical decision-making.

This study discusses the development of deep learning models for diagnosing skin disorders and notes challenges such as lack of data for darker skin tones, without providing new clinical results.

In this study, researchers evaluated deep learning methods for diagnosing Alopecia Areata and found that a modified Inception-Resnet-v2 model achieved a high validation accuracy of 97.94% and loss of 10.4%, suggesting it as an effective tool for classifying alopecia-affected hair.

This study introduced a deep learning framework combining multiple convolutional neural networks to detect scalp and hair disorders and classify hair fall stages, reporting higher precision and robustness in detection and classification compared to individual CNN models.

This study found that a deep learning framework using the ResNet50 model achieved 95% overall accuracy in classifying 10 categories of hair diseases, demonstrating reliable performance but also identifying potential improvements due to misclassifications between similar conditions.

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.

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 study found that their newly developed ADRL framework significantly improved the accuracy of scalp tissue layer segmentation in HR-MR images compared to existing methods.

HairLossMultinet accurately classifies hair damage with 98% accuracy but needs a more diverse dataset for broader use.

This paper presents a new method using Convolutional Neural Networks for detecting hair and scalp diseases, aiming to enhance diagnostics accessibility through a web-based platform integration.

This study reports that an Explainable AI-based hair health prediction system using a novel hybrid model outperformed traditional machine learning methods, achieving high accuracy in predicting key factors and providing personalized recommendations.

This study developed a deep-learning model that accurately diagnosed alopecia areata with an accuracy of 88.92% and distinguished its activity levels with an accuracy of 83.33%, highlighting the potential for artificial intelligence in improving the diagnosis and treatment of this autoimmune hair loss condition.

This study found that a deep learning model can potentially improve the diagnosis and staging of alopecia areata with high accuracy and reliability.

This study identified a four-gene loop as a non-invasive biomarker that selectively activates in alopecia areata, providing a precise target for JAK inhibitor treatments.

This study introduced a multitask learning method to identify shortcut learning in clinical ML systems, revealing it's not always responsible for unfairness and emphasizing the necessity of comprehensive fairness approaches in medical AI.

In this scoping review, researchers observed that while AI-assisted trichoscopy holds promise for standardized assessments of hair and scalp disorders, its clinical translation is limited by small proprietary datasets, inconsistent validation protocols, and a scarcity of real-world clinical studies.

This study reports that deep learning models, particularly CNN and FCN, achieved high accuracy in diagnosing scalp and skin disorders, suggesting potential for improved diagnostic systems with further advancements.

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 review examines advancements in deep learning methods for detecting dermatological conditions from dermoscopic images, summarizing available datasets and suggesting future research directions, but reports no new results.

This study reports that the ScalpEye system accurately diagnosed dandruff, folliculitis, hair loss, and oily hair with a precision range of 97.41% to 99.09%.

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 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 developed a deep learning model using the VGG architecture to predict hair disorders and provide tailored therapeutic suggestions, showing reliable recognition of conditions like dandruff, fungal infections, and alopecia by analyzing images of hair and scalp.

This study found that a combined machine learning model outperformed individual networks in diagnosing scalp conditions using visual data, enhancing prediction accuracy and early detection, particularly in settings with limited resources.

In this study, researchers developed a deep learning model that efficiently classifies five degrees of harm with high accuracy, achieving up to 98% precision, recall, and F1-score across various harm levels, indicating strong potential for practical application in automated harm evaluation.