Evidence map›Paper›PMID 42058346›Full record

ArticleCureus2026

Diversity and Content Reliability Among Dermatologist Influencers on TikTok: A Cross-Sectional Study.

Madison S Meyer, Shoshanah Lasry, Khalid Zakaria

Abstract read
In one paragraph

Article in Cureus, 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

3 authors.

Madison S MeyerFamily Medicine, Wayne State University School of Medicine, Detroit, USA.
Shoshanah LasryMedicine, Nova Southeastern University Dr. Kiran C. Patel College of Osteopathic Medicine, Davie, USA.
Khalid ZakariaInternal Medicine, Henry Ford Health System, Novi, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

TikTok has surged in popularity as a primary source of entertainment for Americans, with many physicians leveraging the platform to disseminate up-to-date medical information. Within this landscape, dermatology has emerged as one of the most sought-after medical subjects, leading to the prominence of numerous board-certified dermatologists as influential figures. However, despite this visibility, dermatology remains among the least diverse medical specialties. This cross-sectional study investigates patterns of following and content reliability on TikTok, particularly concerning dermatological information, based on the racial, gender, and sexual orientation diversity of top influencers. Through qualitative and quantitative analysis of the top 55 dermatologist influencers on TikTok, based on data collected on October 10, 2022, we assess the demographics and video characteristics, with a focus on follower count. Our study found that content produced by Latinx and African American dermatologists demonstrated relatively higher DISCERN scores, although overall content reliability across all groups was low. Additionally, our findings underscore a significant lack of minority representation among dermatology influencers on TikTok, particularly among Latinx, African American, and LGBTQIA+ physicians. This lack of diversity may limit the availability of culturally representative dermatologic information, highlighting a potential gap that has been associated in prior literature with inequities in care and health outcomes. By addressing these diversity gaps, we can work towards fostering more inclusive and equitable healthcare environments on social media platforms.

Indexed as

diversity in dermatologylgbtqia+sexual and gender minority physiciansskin of colorsocial media

Identifiers

PMID42058346
PMCPMC13123124

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Registered trials

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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.