ARTIFICIAL INTELLIGENCE-ASSISTED TRICHOSCOPY IN HAIR DISORDERS: CURRENT APPLICATIONS, CLINICAL UTILITY, AND FUTURE PERSPECTIVES

Martyna Wiśniewska; Natalia Strugała; Magdalena Bulenda; Michał Kostrzewski; Jakub Skrzeczyna; Klaudia Bednarska; Julia Żurek; Marcela Noworolska; Daria Wojtaszkiewicz; Nina Ciećkiewicz; Ewelina Sapała

doi:10.31435/ijitss.3(51).2026.6002

Authors

Martyna Wiśniewska Medical Student, Cardinal Stefan Wyszyński University in Warsaw, Poland https://orcid.org/0009-0001-2858-5691
Natalia Strugała Medical Student, Cardinal Stefan Wyszynski University in Warsaw, Poland https://orcid.org/0009-0006-9502-6343
Magdalena Bulenda Medical Student, Cardinal Stefan Wyszynski University in Warsaw, Poland https://orcid.org/0009-0008-8574-1540
Michał Kostrzewski Medical Student, Jagiellonian University Medical College in Krakow, Poland https://orcid.org/0009-0000-1670-2849
Jakub Skrzeczyna Medical Student, Jagiellonian University Medical College in Krakow, Poland https://orcid.org/0009-0001-4431-1527
Klaudia Bednarska Medical Student, Jagiellonian University Medical College in Krakow, Poland https://orcid.org/0009-0007-9546-8966
Julia Żurek Medical Student, Jagiellonian University Medical College in Krakow, Poland https://orcid.org/0009-0006-0413-645X
Marcela Noworolska Medical Student, Jagiellonian University Medical College in Krakow, Poland https://orcid.org/0009-0000-1773-085X
Daria Wojtaszkiewicz Medical Student, Jagiellonian University Medical College in Krakow, Poland https://orcid.org/0009-0002-3362-9780
Nina Ciećkiewicz Medical Student, Jagiellonian University Medical College in Krakow, Poland https://orcid.org/0009-0001-2733-5885
Ewelina Sapała Medical Student, Jagiellonian University Medical College in Krakow, Poland https://orcid.org/0009-0008-3335-9104

DOI:

https://doi.org/10.31435/ijitss.3(51).2026.6002

Keywords:

Artificial Intelligence, Trichoscopy, Alopecia, Hair Disorders, Machine Learning, Teledermatology

Abstract

Hair and scalp disorders are commonly assessed through visual examination, serial photography, and trichoscopy. Although trichoscopy offers a non-invasive view of follicular and perifollicular structures, its interpretation may vary according to clinician experience, image quality, and examination protocols. Artificial intelligence (AI), including machine learning, deep learning, and computer vision, may help convert selected elements of trichoscopic assessment into more objective and reproducible measurements. This review summarizes the current role of AI-assisted trichoscopy in the diagnosis, follow-up, and individualized management of hair disorders.

A narrative review with a structured literature search was conducted. Publications addressing AI, trichoscopy, alopecia, scalp disorders, dermatological image analysis, teledermatology, and precision medicine were considered. The reviewed literature suggests that the most immediately applicable use of AI in trichology is not autonomous diagnosis, but automated quantification of hair and scalp parameters, including hair density, shaft diameter, follicular unit count, miniaturization, and longitudinal change. These tools may support assessment of androgenetic alopecia, alopecia areata, cicatricial alopecias, and selected inflammatory or infectious scalp conditions. However, diagnostic and predictive models remain limited by heterogeneous datasets, inconsistent image acquisition, limited external validation, and unresolved ethical and regulatory issues.

At present, AI-assisted trichoscopy should be viewed as an adjunct to dermatological evaluation. Future progress will depend on diverse datasets, standardized imaging, explainable algorithms, secure data handling, and clinical validation demonstrating measurable benefit for patients and clinicians.

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