Evidence map›Paper›PMID 42813187›Full record

ArticleSkin health and disease2026

Machine learning applications in androgenetic alopecia: a systematic review.

Jay Huang, Eric McMullen, Sana Gupta, Katya Peri, Miranda Branyiczky, Joy Soliman, Daniel Rayner, Ilya Mukovozov, Jeffrey Donovan

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Skin health and disease, 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

9 authors.

Jay HuangPost-Baccalaureate Health Professions Program, University of California Berkeley, Berkeley, CA, USA.
Eric McMullenDivision of Dermatology, Department of Medicine, Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada.ORCID https://orcid.org/0000-0001-9961-7999
Sana GuptaFaculty of Medicine, University of Ottawa, Ottawa, ON, Canada.
Katya PeriFaculty of Medicine, McGill University, Montreal, QC, Canada.
Miranda BranyiczkyMichael G. DeGroote School of Medicine, McMaster University, Hamilton, ON, Canada.ORCID https://orcid.org/0009-0000-4020-4532
Joy SolimanFaculty of Medicine, University of Ottawa, Ottawa, ON, Canada.
Daniel RaynerSchulich School of Medicine & Dentistry, Western University, London, ON, Canada.
Ilya MukovozovDivision of Dermatology, Department of Medicine, Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada.
Jeffrey DonovanDepartment of Dermatology and Skin Science, University of British Columbia, Vancouver, BC, Canada.ORCID https://orcid.org/0000-0002-8623-2974

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

As applications of artificial intelligence (AI) continue to grow in dermatology, it is necessary to understand the potential of artificial intelligence, including machine learning, in the treatment of androgenetic alopecia. Accordingly, this study aims to evaluate AI applications for AGA diagnosis, severity assessment and predicting treatment outcomes.

Identifiers

PMID42813187
PMCPMC13623419

What OpenQuestion holds

Textmetadata
Read underepoch 390

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.