Evidence map›Paper›PMID 40591107›Full record

ArticleJournal of cancer education : the official journal of the American Association for Cancer Education2026

Assessing Readability of Skin Cancer Screening Resources: A Comparison of Online Websites and ChatGPT Responses.

Elissa Goorman, Sukul Mittal, Jennifer N Choi

Abstract readComparative Study
In one paragraph

Article in Journal of cancer education : the official journal of the American Association for Cancer Education, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Article
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.

Elissa Goorman *Department of Dermatology, Northwestern University Feinberg School of Medicine, Chicago, IL, USA.
Sukul Mittal *Department of Dermatology, Northwestern University Feinberg School of Medicine, Chicago, IL, USA.
Jennifer N ChoiDepartment of Dermatology, Northwestern University Feinberg School of Medicine, Chicago, IL, USA. jennifer.choi@northwestern.edu.ORCID 0000-0003-4897-3277

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Effective communication is essential for promoting appropriate skin cancer screening for the public. This study compares the readability of online resources and ChatGPT-generated responses related to the topic of skin cancer screening. We analyzed 60 websites and responses to five questions from ChatGPT-4.0 using five readability metrics: the Flesch-Kincaid Reading Ease, Flesch-Kincaid Grade Level, SMOG Index, Gunning Fog Index, and Coleman-Liau Index. Results showed that both websites and ChatGPT responses exceeded the recommended sixth grade reading level for health-related information. No significant differences were found between the readability for university-hosted versus non-university-hosted websites. However, across all readability metrics, ChatGPT responses were significantly more difficult to read. These findings highlight the need to enhance the accessibility of health information by aligning content with recommended literacy levels. Future efforts should focus on developing patient-centered, publicly accessible materials and refining AI-generated content to improve public understanding and encourage proactive engagement in skin cancer screenings.

Indexed as

ComprehensionEarly Detection of CancerHealth LiteracyInternetSkin NeoplasmsHumansArtificial intelligenceChatGPTHealth literacyOnline health informationPatient educationReadabilitySkin cancer screening

Identifiers

PMID40591107
PMCPMC13222313

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.