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In this study, researchers developed a method for high-resolution live imaging of leg regeneration in the crustacean Parhyale hawaiensis, which effectively captures the entire process at cellular resolution over 10 days while minimizing photodamage.

This study identified the Arabidopsis cation chloride cotransporter CCC1 as essential for regulating pH and function in the trans-Golgi network/early endosome, with its absence causing significant growth and stress response defects.

This study found that the Arabidopsis cation chloride cotransporter (CCC1) is crucial for regulating pH and processes in the trans-Golgi-network/early endosome, impacting plant growth and stress responses.

This study reports that improvements to the EczemaNet pipeline, incorporating pixel-level segmentation and data augmentation, enhanced the reliability and interpretability of assessing atopic dermatitis severity from digital images.

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.

This survey investigated how well an AI chatbot could generate dermoscopic language reports for dermatologists and found that participants were equally satisfied with its responses across scenarios, despite lower performance in diagnosing SCC and inflammatory dermatoses.

AI can accurately measure hair loss severity in alopecia areata.

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 observed that GPT-4o, a generative AI model, showed high concordance with human providers in automating image-based SALT scores for assessing alopecia areata, suggesting its potential in assisting clinical evaluations without additional training.

This study found that GPT-4o demonstrated high concordance with in-person and image-based provider assessments in generating SALT scores for alopecia areata, suggesting its potential as an adjunct tool in clinical practice for automating these assessments.

This study evaluated AI chatbots' responses to scabies-related questions, finding DeepSeek had the highest accuracy despite higher hallucination rates, while ChatGPT-5.2 provided more readable and reliable information, highlighting variability and the need for cautious use of AI in health queries.

This study explored AI's potential in trichology services, finding it beneficial for diagnostics and treatment supervision, but noted the importance of addressing legal considerations, particularly in the Polish market.

In this study, the Tibot AI application showed high diagnostic accuracy for adnexal and pigmentary disorders and cutaneous tumors in dermatology, but was less effective for immunological disorders and infestations, suggesting the need for further dataset refinement.

Patients found AI helpful for alopecia diagnosis but want it to support, not replace, doctors.

In this study, the integration of artificial intelligence in medicine was discussed, highlighting its potential to enhance diagnostic processes, optimize therapies, and provide advanced patient monitoring despite challenges like data inconsistency and limited model transparency.

This study demonstrates that an AI-based grading framework using a novel area ratio metric improves the accuracy and consistency of male pattern hair loss classification, especially in advanced grades, compared to traditional methods.

In this study, the DIET-AI model, developed from a large dataset of over 200,000 images, demonstrated diagnostic performance for 31 skin diseases comparable to dermatologists of varying experience levels in 15 hospitals across China, supporting its potential effectiveness in clinical settings.

This study describes an AI-powered system for diagnosing dermatological conditions, achieving a weighted score of 0.87 in both contextual understanding and diagnostic accuracy, suggesting it could enhance tele-dermatology applications by supporting remote consultations and care access in underserved regions.

This study introduces ALUDWIG, an automated tool for assessing female androgenic alopecia severity from smartphone images, which may offer a more consistent alternative to current scoring methods like the Ludwig scale.

This study reviewed the current status and future scope of artificial intelligence in healthcare, highlighting its potential to revolutionize medical practices through improved affordability, efficiency, and speed, as well as its applicability in various fields such as imaging, diagnosis, and individualized care.

This study detailed the implementation of an autonomous AI device in an NHS skin cancer pathway, showing that it achieved a sensitivity of 97.3% for diagnosing skin cancers and exceeded sensitivity targets compared to specialists with a negative predictive value over 99.7%.

In this study, artificial intelligence's integration into Indian aesthetic medicine has expanded from consumer apps to crucial roles in diagnostics, treatment planning, and clinic management, driven by demands for precise data over subjective opinions and enhancing competitive advantage.

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.

This review discusses the integration of Artificial Intelligence in non-surgical cosmetic procedures, highlighting its potential to improve personalized aesthetic care and operational efficiency while addressing ethical and regulatory challenges; it presents no new clinical findings.

ChatGPT is preferred for creating dermatology patient handouts, but all models can be useful with oversight.

This review found that AI in dermatology shows high diagnostic accuracy comparable to experienced clinicians, but integration into clinical practice faces challenges requiring further research.

This study found that artificial intelligence assistance improved diagnostic accuracy in dermatologic cases for primary care physicians and nurse practitioners, with higher agreement rates with reference diagnoses observed.

This paper critically evaluates the role of AI in cosmetic surgery, highlighting its potential to enhance precision, tailor treatment, and improve patient outcomes, while also addressing ethical, legal, and regulatory challenges that complicate its integration into clinical practice.

In this study, researchers aim to use AI-related methods to predict different hair loss patterns, including male and female pattern baldness, alopecia areata, telogen effluvium, and traction alopecia, though specific results are not reported.

This study found that while DALL-E 2 can create realistic hair images from text prompts, its accuracy in depicting specific hair disorders, like alopecia areata, often falls short, highlighting the need for AI developers to work with medical experts to improve dermatological applications.