Patient Perspectives regarding Artificial Intelligence-Driven Trichoscopy for Alopecia Evaluation
Year: 2025
Type: article
Study design: Observational Study
Abstract: Introduction: Alopecia treatment can improve patients’ quality of life, but slow hair regrowth makes progress difficult to detect. Trichoscopic measurements (TMs; hair density, hairs/cm2; hair caliber, µm) provide an objective method for monitoring treatment response. Artificial intelligence (AI) enables trichoscopic analysis without hair clippings. This study examines the perspectives of alopecia patients regarding AI-driven trichoscopy. Methods: We conducted an IRB-approved survey study and chart review of 101 patients at the NYU Langone hair clinic undergoing AI-driven trichoscopy. Pre- and post-visit surveys measured self-perceived alopecia severity and quality-of-life impact before and after reviewing TMs with their dermatologist. Opinions regarding AI-driven trichoscopy were assessed via Likert scales. Demographics, medical history, and clinician-assessed severity were extracted from medical charts. Results: Most participants were female (74.3%), white (73.3%), and had nonscarring alopecia (89%). After reviewing TMs, 33.7% of participants reported feeling less bothered by their alopecia, 39.6% perceived reduced severity, and 54.5% noted increased treatment motivation. Among follow-up patients, concordance between patient and physician assessments of interval change in hair loss improved after viewing TMs (p = 0.0075). Patients endorsed enhanced alopecia care and greater confidence in treatment recommendations with the use of AI-driven trichoscopy. Conclusion: Patients with alopecia generally view AI-driven trichoscopy favorably, noting improved quality of care and greater motivation to continue therapy.
Source: Skin Appendage Disorders
DOI: 10.1159/000550043
Authors: Caitlin A. Kearney, Anna L. Brinks, Carli N. Lawrence, Jerry Shapiro, Mario E. Lacouture, Kristen I. Lo Sicco
Language: English
Topic: Hair Growth and Disorders
Subfield: Urology
Field: Medicine
Domain: Health Sciences
Open Access status: hybrid
OpenAlex ID: W4417299861