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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 reviews the transformative role of artificial intelligence in biomaterial design, highlighting its ability to reduce costs through virtual screening, enhance material performance, and predict biological interactions to advance personalized and precision medicine.

In this review, recent innovations in hair transplantation, such as AI-assisted graft selection, robotic systems, stem cell therapy, and advanced drug delivery techniques, are reported to enhance hair density and thickness for more natural results and higher patient satisfaction by addressing limitations of traditional methods.

This study reported that pathways related to apoptosis, cell proliferation, and WNT signaling might be key drivers of hair loss in androgenetic alopecia, guiding potential targets for therapy development.

This study developed a non-invasive, partially automated protocol for extracting high-quality DNA from hair follicles of marmosets, significantly reducing chimerism rates compared to blood, and proving reliable for whole genome sequencing in low-input DNA scenarios.

This study introduces an extended approach to the Bonferroni procedure that accounts for correlations among endpoints, aiming to improve test power while maintaining strong control of the family-wise type I error rate in clinical trials.

This study found that Super Merino sheep have a higher wool follicle density, finer fleece, and distinct gene expression compared to Small Tail Han sheep, which may inform future breeding and genetic interventions.

This study used integrated single-cell chromatin and transcriptomic analyses in developing mouse skin to uncover gene networks involved in skin lineage specification and identified Mef2c+ upper fibroblasts as potential precursors to certain muscle-like structures, with cross-species findings in human skin.

This study developed an AI-driven method for classifying cells in Follicular Lymphoma cases, achieving a 63% F1-score, precision, and recall in distinguishing centroblasts from other cell types using whole slide images at x20 resolution.

This study describes a new treatment algorithm for alopecia areata that includes both disease severity and quality-of-life considerations, highlighting recent therapeutic advances and the need for future validation and access equity.

This review compiles existing data on frontal fibrosing alopecia and highlights the promise of 5-alpha reductase inhibitors as a treatment option, while noting the need for clarity on its cause and progression.

This case report suggests that lisinopril may cause alopecia, which resolved after the patient switched to losartan potassium.

This study reviews methods to ensure the reliability of machine learning models in medical imaging, focusing on bias detection, data drift assessment, and accuracy estimation without ground truth labels to enhance integration into clinical settings.

This study found that ensemble machine learning models effectively predict hair fall by combining the strengths of individual algorithms, leading to higher accuracy, precision, and recall in identifying hair and non-hair fall instances compared to single algorithms.

In this study, the authors used AI-driven methods to identify and prioritize promising therapeutic targets that may address both aging and Glioblastoma Multiforme, proposing CNGA3, GLUD1, and SIRT1 as novel candidates.

This study evaluated machine-learning models to predict PCOS among reproductive-aged women in Bangladesh, finding that the XGBoost model achieved high accuracy (99.63%) and effectiveness, particularly when prioritizing clinical features over psychological ones in the predictive process.

This study observed that while TikTok offers the best flow for videos on laryngeal cancer, YouTube provides higher quality, but video quality overall needs professional enhancement and platform algorithm improvements.

In this paper, Chemistry42—a software integrating AI with computational and medicinal chemistry—demonstrated efficiency in designing novel molecular structures targeting DDR1 and CDK20, with properties validated in both in vitro and in vivo studies.

This literature review explores the role of biomarkers in personalized medicine for various dermatological conditions like Hidradenitis Suppurativa, Psoriasis, and Atopic Dermatitis, highlighting how they guide targeted therapies and the limitations of current markers in specificity, as reported by the authors.

This study describes the PandaOmics platform, which uses AI and bioinformatics to identify new therapeutic targets and biomarkers for various diseases, demonstrating validation in laboratory and animal studies.

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 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 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.

In this study, the XGBoost algorithm successfully diagnosed polycystic ovary syndrome with an accuracy of 0.87 using a dataset from Kerala, suggesting its usefulness for classification problems in healthcare.

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 discusses the clinical efficacy, safety, and mechanisms of Autologous Micrografting Technology for androgenetic alopecia, reporting significant short-term improvements in hair density and thickness.

In this study, researchers used Google Search ads to gather an open access dataset of 10,408 dermatological images from over 5,000 U.S. internet users, enhancing the diversity and representativeness of skin condition images available for research and artificial intelligence development.

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 review suggests that the compound DRDE-07, originally a sulphur-mustard countermeasure candidate, could potentially be repurposed for skin protection due to its compatibility with pathways involved in oxidative stress and inflammation, though further experimental study is needed to confirm its therapeutic applicability in dermatology.

This review highlights recent advances in personalized transdermal drug delivery systems, noting their potential for improved treatment efficacy and safety, but identifies significant challenges in clinical translation.