Evidence map›Paper›PMID 41758324›Full record

ReviewDermatology and therapy2026

Artificial Intelligence in Hair Disorders: A Narrative Review.

Alba Lecumberri, Sergio Vañó-Galván, Daniel Asz-Sigall, David Saceda-Corralo

Abstract readReview
In one paragraph

Review in Dermatology and therapy, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Alba LecumberriDermatology Department, Hospital Universitario Ramón y Cajal, IRYCIS, Universidad de Alcalá, Madrid, Spain. albalecumberri@gmail.com.ORCID http://orcid.org/0009-0009-8444-9051
Sergio Vañó-GalvánDermatology Department, Hospital Universitario Ramón y Cajal, IRYCIS, Universidad de Alcalá, Madrid, Spain.ORCID http://orcid.org/0000-0003-2773-7494
Daniel Asz-SigallHair Clinic, Hospital General Manuel Gea González, Mexico City, Mexico.ORCID http://orcid.org/0000-0003-1848-5686
David Saceda-CorraloDermatology Department, Hospital Universitario Ramón y Cajal, IRYCIS, Universidad de Alcalá, Madrid, Spain.ORCID http://orcid.org/0000-0001-6315-5462

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Artificial intelligence (AI) is increasingly being incorporated into dermatology, with hair disorders representing a particularly suitable field for its application. The assessment and management of alopecias rely heavily on visual evaluation, pattern recognition, longitudinal monitoring, and integration of heterogeneous clinical and biological data; processes that are often limited by interobserver variability and incomplete standardization. In recent years, AI-based approaches have been applied across multiple domains in the clinical assessment of hair disorders, including objective quantitative assessment, diagnostic support, personalized medicine, drug development, robotic-assisted procedures, and analysis of patient-reported outcomes and clinical communication. Among these applications, tools enabling automated and reproducible measurement of hair parameters are currently the most established in clinical practice, whereas diagnostic and predictive models remain largely investigational. This narrative review summarizes current and emerging applications of artificial intelligence in hair disorders, with a focus on their clinical relevance and practical implementation. In addition, methodological limitations, ethical considerations, and challenges related to external validation, interpretability, and data bias are discussed. Although AI-based tools are not yet able to replace clinical judgement, they hold significant potential to complement dermatologists' expertise and improve objectivity, efficiency, and personalization in the management of hair disorders.

Indexed as

AlopeciaHair lossMachine learningNeural networksTrichology

Identifiers

PMID41758324
PMCPMC13013867

What OpenQuestion holds

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LicenceCC BY-NC
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.