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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 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.

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.

This review highlights current and future AI applications in hair restoration and diagnosis of hair disorders, including automated systems for hair detection and self-diagnosis, emphasizing the need for experts to understand their benefits and limitations.

This study developed a convolutional neural network model for non-invasive diagnosis of androgenetic alopecia using dermoscopic images, demonstrating potential accuracy and scalability while highlighting the importance of model interpretability for clinical use.

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

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 review outlines the key principles and clinical applications of multimodal non-invasive skin imaging technologies, emphasizing their combined strengths in enhancing diagnosis and treatment assessment in dermatology.

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 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 suggests that while AI shows promise as a diagnostic tool for assessing disease severity in dermatology, understanding its limitations is essential before broad clinical adoption, and it identifies gaps in research that need addressing.

This research discusses the shift in cosmetic dermatology towards integrating regenerative medicine, AI, and personalized care, highlighting their potential to improve aesthetic outcomes and skin function while considering ethical and regulatory challenges.

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 used bibliometric analysis to evaluate global research on AI applications in dermatology, identifying 406 relevant papers and highlighting current priorities such as machine learning for wound progression, AI in teledermatology, and applications for skin diseases.

This review discusses the transformative potential and challenges of using AI in cosmetic dermatology, highlighting how AI is enhancing diagnostics, treatment personalization, and patient satisfaction while addressing issues such as data quality and ethical concerns in order to ensure responsible and ethical implementation.

This article describes current advancements and future possibilities in skin imaging technology, teledermatology, and AI in dermatology, without reporting new research results.

This study discusses the development of AI in dermatology and cosmetology, noting its use in skin cancer diagnosis and personalized cosmetics, while highlighting uncertainties about whether recent advancements represent significant medical progress or are influenced by marketing strategies.

This review highlights the need for inclusive dermatology practices to address variations in skin conditions globally by incorporating tools like AI, teledermatology, and culturally competent education.

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 discusses the potential of advanced imaging technologies in dermatology to improve diagnostic accuracy and reduce the need for invasive procedures like biopsies, while noting significant barriers in adoption and accessibility in India due to costs and infrastructure constraints.

ChatGPT is more accurate at diagnosing hair disorders in lighter skin tones than darker ones.

Personalized medicine is important for treating skin disorders, with new treatments and connections to hormones and genetics being explored.

This review discusses hirsutism in women with PCOS who present with normal androgen levels and highlights diagnostic challenges, reporting no new clinical findings.

This case report identified a connection between idiopathic intracranial hypertension, papilledema, and newly diagnosed systemic lupus erythematosus in a young woman, highlighting the importance of thorough system reviews in such circumstances.

This study highlights the potential of novel non-invasive testing techniques to enhance the diagnosis, treatment, and care of scalp hair diseases, urging future research to improve their accuracy and efficiency.

This study reviews the recent advancements in artificial intelligence applications in dermatology and dermatopathology, highlighting its role in diagnosing skin cancer, its impact on clinical investigations, and the potential and limitations of AI technology within these fields.

In this retrospective study, researchers found that the diagnostic yield for alopecia skin biopsies was lower with longer durations of disease onset and suggested improving diagnostic accuracy through standardized request forms and digital pathology integration.

This article highlights both the opportunities and challenges of using big data in dermatology, noting potential benefits like improved diagnostics and public health monitoring, alongside challenges such as data quality and AI training disparities.

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.

In this study, a modified Xception deep learning model achieved a 92% accuracy rate in diagnosing hair and scalp disorders, significantly outperforming other models, suggesting AI could improve dermatological diagnostics' accuracy and accessibility.