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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 narrative review found that while AI-based tools in dermatology, particularly for hair disorder assessment, have potential to enhance clinical practice by improving objectivity and personalization, they currently serve mainly a complementary role and face challenges like methodological limitations and data bias.

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 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 review examines the use of computer vision techniques, specifically deep learning architectures and image processing algorithms, for detecting and assessing skin conditions like vitiligo and dermatitis, and highlights the need for disease-specific datasets to improve automated diagnostic tools in dermatology.

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.

This study found that the HairComb algorithm achieved high accuracy in quantifying percentage hair loss across various types of alopecia, suggesting its potential for standardized automated assessments.

This study proposes a method for automatically removing hairs from skin lesion images by using edge-tangent flow for hair detection and texture synthesis for restoring occluded regions with minimal artifacts.

Dermoscopy is useful for many health professionals, not just dermatologists, in improving skin condition diagnoses and reducing unnecessary biopsies.

This preliminary study suggests that Raman micro-spectroscopy can help differentiate basal cell carcinoma from hair follicles in skin tissue sections, although some misclassification of hair follicles as carcinoma was observed.

The ProScope HR is an effective, user-friendly, and affordable tool for diagnosing hair loss.

This study developed a high-resolution phototrichogram system that can automatically and accurately assess hair growth metrics in cosmetic trials, achieving over 90% correlation with manual measurements.

This study developed a novel system that can automatically estimate hair loss parameters from images without expert input, reporting satisfactory results from testing on samples collected in Kolkata.

This article reviews various diagnostic tools for hair disorders, describing methods ranging from invasive to noninvasive, but reports no new research findings or clinical results.

In this narrative review, the authors highlighted that AI-supported electronic health record analysis could help recognize Polycystic Ovary Syndrome/Polyendocrine Metabolic Ovarian Syndrome earlier, but also warned that these systems might perpetuate historical biases if not properly validated and interpreted.

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 study developed a deep learning model for the automated early detection of androgenetic alopecia using trichoscopic images, and found it demonstrated high accuracy and generalizability in a Korean clinical cohort, achieving a 90% accuracy in external validation.

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.

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 study explored deep learning's potential in diagnosing scalp conditions like alopecia, psoriasis, and folliculitis, using a two-dimensional Convolutional Neural Network, achieving high accuracy and precision despite challenges of a small and uneven dataset.

This article highlights India's suitability as a strategic location for dermatology clinical trials due to its diverse patient population and cost-effective environment, offering insights on optimizing trials through advanced diagnostics, regulatory compliance, and ethical governance to attract global sponsors.

This article discusses phototrichogram education and patient satisfaction in androgenetic alopecia but reports no new clinical findings.

This review examines nanozymes as promising tools for cancer diagnosis and therapy due to their enzyme-mimicking catalytic properties and highlights their challenges, including biosafety and clinical translation, alongside potential improvements through biomimetic designs, particularly utilizing exosome-based strategies for targeted delivery to tumors.

This review reported that exosomes have significant potential in dermatology for use as diagnostic markers and therapeutic tools, with implications for treating conditions like inflammatory skin diseases, skin aging, wound healing, and hair restoration.

A new computer tool quickly measures hair thickness differences in people with common types of hair loss.

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 concluded that TrichoScan is error-prone in its current form and overestimates certain hair growth parameters, with results not aligning with clinical severity of alopecia.

This review highlights the light microscopic features of various infectious and non-infectious hair conditions and reports no new clinical results.

This research introduces WomenCare, a web-based system using a machine learning model to predict PCOD risk by evaluating factors like age, BMI, and lifestyle habits; it aims to help women monitor their health but is not a substitute for a professional diagnosis.