Artificial Intelligence in Hair Disorders: A Narrative Review

February 2026 in “ Dermatology and Therapy ”

A. Lecumberri, Sergio Vaño-Galvan, Daniel Asz-Sigall, David Saceda-Corralo

artificial intelligence AI dermatology hair disorders quantitative assessment diagnostic support personalized medicine drug development robotic procedures automated measurement tools diagnostic models

Studysummary 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. Our plain-language summary of this paper — not a Tressless recommendation.

Artificial intelligence (AI) is being integrated into dermatology, particularly for hair disorders, due to its ability to enhance visual evaluation, pattern recognition, and data integration. AI applications in hair disorders include quantitative assessment, diagnostic support, personalized medicine, drug development, robotic procedures, and patient communication. Automated measurement tools are well-established, while diagnostic models are still under investigation. The review highlights AI's potential to complement dermatologists by improving objectivity, efficiency, and personalization, despite current limitations in replacing clinical judgment. Methodological challenges, ethical issues, and data bias are also discussed.

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research Early Prediction of Alopecia Areata Using Machine Learning Modeling of Neuro Stress Immune Signatures from Multiple Datasets

1 citations , December 2025 in “Scientific Reports”

In this study, researchers developed a predictive model for the onset of alopecia areata by analyzing six datasets to identify key feature genes and employing various machine learning algorithms, ultimately finding the XGBoost model most effective for clinical application.

research Analysis of Trichoscopic Images Using Deep Neural Networks for the Diagnosis and Activity Assessment of Alopecia Areata – A Retrospective Study

2 citations , September 2025 in “JDDG Journal der Deutschen Dermatologischen Gesellschaft”

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

research Machine Learning Predicts Scarring Progression in Lichen Planopilaris: A Multidimensional Model Integrating Trichoscopy, Vitamin D, and Diagnostic Timelines

September 2025 in “International Journal of Medical Informatics”

A machine learning model can predict scarring in lichen planopilaris using factors like vitamin D levels and diagnostic delay.

Natural Product-Inspired Bis(Trifluoromethyl) Phenyl Hydroxycinnamate Derivatives as Promising Nonsteroidal Inhibitors of Human Steroid 5α-Reductase Type-1: Synthesis, In Vitro, and In Silico Studies

research Natural Product-Inspired Bis(Trifluoromethyl) Phenyl Hydroxycinnamate Derivatives as Promising Nonsteroidal Inhibitors of Human Steroid 5α-Reductase Type-1: Synthesis, In Vitro, and In Silico Studies

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This study synthesized and evaluated hydroxycinnamate derivatives for their ability to inhibit human SRD5A1, finding that three compounds showed significant inhibitory activity and low cytotoxicity. Compound 10a notably reduced SRD5A1 protein expression in cells, suggesting potential for nonsteroidal treatment of androgen-related conditions.

Microbial Dysbiosis and Its Diagnostic Potential in Androgenetic Alopecia: Insights from Multi-Kingdom Sequencing and Machine Learning

research Microbial Dysbiosis and Its Diagnostic Potential in Androgenetic Alopecia: Insights from Multi-Kingdom Sequencing and Machine Learning

May 2025 in “mSystems”

This study discovered that androgenetic alopecia disrupts the scalp microbiome balance across the whole scalp, not just areas with hair loss, and introduced a microbial index for early detection and severity prediction.

research Performance Exploration of Multi-Gene Panels of Alopecia Areata Susceptibility and Drug-Binding Targets

1 citations , March 2025 in “Frontiers in Physiology”

This study identified key genes linked to immune cells and potential therapeutic compounds for alopecia areata by evaluating upregulated genes from patient datasets, highlighting T and NK cell involvement in hair follicle attack and suggesting drug candidates through molecular docking and dynamics simulations.

Enhanced Hair Regrowth with Five Monthly Sessions of Minoxidil-Dutasteride-Copper Peptides Tattooing for Androgenetic Alopecia Assessed by Artificial Intelligence and Blinded Evaluators

research Enhanced Hair Regrowth with Five Monthly Sessions of Minoxidil-Dutasteride-Copper Peptides Tattooing for Androgenetic Alopecia Assessed by Artificial Intelligence and Blinded Evaluators

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Five monthly sessions of minoxidil-dutasteride-copper peptide tattooing significantly improve hair regrowth in men with androgenetic alopecia.

research Artificial Intelligence-Based Personalization of Treatment Regimen for Hair Loss: A 6-Month Clinical Trial

February 2025 in “PubMed”

This study evaluated an AI-driven system for personalizing non-medicated hair loss treatments in women, showing significant improvements in hair growth, shedding, texture, and scalp health over 24 weeks, with no adverse effects reported.

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research A Comparative Study on the Application of Robotic Hair Restoration Technology Versus Traditional Follicular Unit Excision in Male Androgenetic Alopecia

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This report describes ARTAS, a robotic hair transplant technology with advanced mechanical and recognition capabilities, which has gained popularity in China since 2016 due to its ease of use and efficient hair follicle harvesting.

research A Top-Down Design Approach for Generating a Peptide PROTAC Drug Targeting Androgen Receptor for Androgenetic Alopecia Therapy

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This study developed a method for designing peptide-based PROTAC drugs using advanced AI models, successfully identifying peptides that bind to specific receptors and incorporating them into a potential transdermal treatment for androgenetic alopecia.

research Using Artificial Intelligence to Compute Severity of Alopecia Tool Scores

5 citations , April 2024 in “JAAD International”

AI can accurately measure hair loss severity in alopecia areata.

research Use of Artificial Intelligence to Track Platelet-Rich Plasma Treatment Outcomes in Females With Nonscarring Alopecia: A Case Series

2 citations , March 2024 in “JAAD Case Reports”

In this study, platelet-rich plasma treatments over three months were reported to improve hair growth in individuals with androgenetic alopecia, although objectively measuring its efficacy remains challenging.

research Comparison of Large Language Models in Generating Patient Handouts for the Dermatology Clinic: A Blinded Study

6 citations , February 2024 in “JAAD International”

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

research Identification of Ferroptosis-Related Biomarkers in Alopecia Areata Through Machine Learning

February 2024 in “Scientific reports”

This study identified four ferroptosis-related genes, SLC40A1, LCN2, CREB5, and SLC7A11, as potential diagnostic markers for alopecia areata, revealing reduced expression in affected patients compared to controls, with a predictive model showing high accuracy in differentiating the condition.

Construction of Regulatory Network for Alopecia Areata Progression and Identification of Immune Monitoring Genes Based on Multiple Machine-Learning Algorithms

research Construction of Regulatory Network for Alopecia Areata Progression and Identification of Immune Monitoring Genes Based on Multiple Machine-Learning Algorithms

3 citations , May 2023 in “Precision clinical medicine”

This study analyzed gene expression data to identify key genes involved in severe forms of alopecia areata, discovering four immune monitoring genes (LGR5, SHISA2, HOXC13, S100A3) with potential for early diagnosis and better understanding of the disease's biological mechanisms.

research Autologous Platelet-Rich Plasma for Treating Androgenetic Alopecia: A Novel Treatment Protocol Standardized on 2 Cases

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This study reported that a new PRP application protocol with two treatments enhanced hair growth metrics in the vertex region of androgenetic alopecia patients within six months.

research Machine Learning Guided Discovery of Superoxide Dismutase Nanozymes for Androgenetic Alopecia

3 citations , October 2022 in “Nano Letters”

This study found that a microneedle patch using manganese thiophosphite showed greater hair regrowth potential compared to minoxidil, even with less frequent application, for treating androgenetic alopecia.

Using Artificial Intelligence to Analyze Publicly Available Social Media Posts to Understand Patient Perspectives Toward Specific Treatments of Alopecia Areata

research Using Artificial Intelligence to Analyze Publicly Available Social Media Posts to Understand Patient Perspectives Toward Specific Treatments of Alopecia Areata

1 citations , September 2022 in “JAAD international”

This study analyzed social media posts on alopecia areata treatments and found that while most reported perceived improvement, many treatments, particularly dexamethasone, were associated with underlying negative emotions.

research Evaluation of Automated Measurement of Hair Density Using Deep Neural Networks

8 citations , January 2022 in “Sensors”

This study analyzed deep learning's application to automate hair density measurement in images and found that YOLOv4 had the best performance among tested algorithms, with a mean average precision of 58.67.

research Deep Learning-Based Trichoscopic Image Analysis and Quantitative Model for Predicting Basic and Specific Classification in Male Androgenetic Alopecia

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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.

research Research on Mental Stress Recognition of Depressive Disorders in Patients with Androgenic Alopecia Based on Machine Learning and Fuzzy K-Means Clustering

November 2021 in “Frontiers in Genetics”

This study found that a new FAW-FS algorithm improved recognition of depression in patients with androgenic alopecia, and comprehensive psychological interventions positively impacted their rehabilitation outcomes.

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research Assessment of Hair Density and Caliber in Caucasian and Asian Female Subjects with Female Pattern Hair Loss Using the Folliscope

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This study found that Caucasian female hair is denser than Asian female hair in the occipital region regardless of treatment but thinner in diameter in both the frontal and occipital regions.

research Validation of TrichoScan Technology as a Fully-Automated Tool for Evaluation of Hair Growth Parameters

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This study found that the TrichoScan method for evaluating hair loss showed excellent correlation with manual marking and provided more consistent, reproducible results with less variability and operator error.

research TrichoScan: A Useful Method for Measuring Hair Growth Parameters and Enhancing Patient Selection for Hair Restoration Surgery

1 citations , March 2009 in “Hair transplant forum international”

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research TrichoScan: A Novel Tool for the Analysis of Hair Growth In Vivo

70 citations , June 2003 in “Journal of Investigative Dermatology Symposium Proceedings”

This study reports that the TrichoScan method effectively measures hair growth parameters and detected significant improvements in hair counts and thickness in men with androgenetic alopecia after finasteride treatment.