Evidence map›Paper›PMID 42625996›Full record

ReviewFrontiers in digital health2026

Research and application of digital technology in the diagnosis and treatment of androgenetic alopecia.

Xi Wei, Jingjing Wang, Yicheng Zhang, Xiaojun Yan, Yating Xiao, Yutong Ci, Xiaoling Luo, Xue Wang

Abstract readReview
In one paragraph

Review in Frontiers in digital health, 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

8 authors.

Xi Wei *Department of Dermatology, The First Affiliated Hospital of Shihezi University, Shihezi, Xinjiang, China.
Jingjing Wang *Department of Dermatology, The First Affiliated Hospital of Shihezi University, Shihezi, Xinjiang, China.
Yicheng Zhang *School of Medicine, Shihezi University, Shihezi, Xinjiang, China.
Xiaojun YanSchool of Medicine, Shihezi University, Shihezi, Xinjiang, China.
Yating XiaoSchool of Medicine, Shihezi University, Shihezi, Xinjiang, China.
Yutong CiLibrary, Huazhong University of Science and Technology, Wuhan, Hubei, China.
Xiaoling LuoSubject Service Center, The Library of Shihezi University, Shihezi, Xinjiang, China.
Xue WangDepartment of Dermatology, The First Affiliated Hospital of Shihezi University, Shihezi, Xinjiang, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Androgenetic alopecia (AGA) is the most common type of alopecia in the world. Although AGA may not cause physical damage to the human body, it will bring serious psychological harm and a decline in quality of life. In the past, the diagnosis and treatment of AGA in clinic mostly depended on the experience of doctors. The introduction of digital technology will provide visual images for the hair characterization of AGA, assist the staging evaluation and differential diagnosis of AGA, and shows potential for improving efficiency and accuracy in reported studies under specific conditions. In terms of treatment, laser treatment is an important means of AGA; telemedicine makes the treatment and nursing of AGA more convenient and quick; at the same time, the application of digitization also helps to optimize AGA efficacy evaluation, treatment strategies, and new drug development. In order to further summarize some new diagnosis and treatment ideas and schemes in clinical practice, this paper deeply studies and analyzes the application of digital technology in the diagnosis and treatment of AGA in recent years, aiming to provide a more efficient, convenient and personalized scheme for clinical practice.

Indexed as

androgenetic alopeciaartificial intelligence-assisted diagnosisclinical practicedigital technologytelemedicine

Identifiers

PMID42625996
PMCPMC13490770

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.