Evidence map›Paper›PMID 42109970›Full record

ArticleCureus2026

Assessing the Quality of Artificial Intelligence (AI)-Generated Patient Education for Gender-Affirming Top Surgery Using the Modified Ensuring Quality Information for Patients (mEQIP) Tool.

Adrian O Campos, Mohammed Almeflehi, Sean Kim, Daniel Freet

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.

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0citing papers in PubMed
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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.

Adrian O CamposPlastic Reconstructive Surgery, University of Iowa Hospitals and Clinics, Iowa City, USA.
Mohammed AlmeflehiPlastic Reconstructive Surgery, University of Iowa Hospitals and Clinics, Iowa City, USA.
Sean KimPlastic Reconstructive Surgery, University of Iowa Hospitals and Clinics, Iowa City, USA.
Daniel FreetPlastic Reconstructive Surgery, University of Iowa Hospitals and Clinics, Iowa City, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundLarge language models, such as OpenAI's ChatGPT, have the potential to revolutionize patient education. These platforms allow for a vast collection, analysis, and organization of information largely unavailable to online users. Within medicine, these tools could help complement physicians in better educating patients on complex and routine medical information. Currently, limited literature exists on the reliability of such tools to provide high-quality information to patients inquiring about gender-affirming top surgery. Therefore, this study aimed to evaluate ChatGPT's performance when generating patient-level information on gender-affirming top surgery compared with the current online information provided by the American Society of Plastic Surgery (ASPS) using the Modified Ensuring Quality Information for Patients (mEQIP) tool.

methodsChatGPT-4-generated patient-level education on transmasculine and transfeminine gender-affirming top surgery was compared against current online content provided by the ASPS. ChatGPT-4 patient content was generated by individually formatting standardized mEQIP content items to incorporate the topic of gender-affirming top surgery into ChatGPT-4, with responses recorded for each item. Four experts in gender-affirming top surgery independently rated both sources using a 36-item mEQIP tool. Paired t-tests comparing overall and content-specific mEQIP scores of the ChatGPT-4 and ASPS material were then estimated to measure the quality of the content. The effect size between the two groups was evaluated using Cohen's d. Lastly, Cronbach's alpha and ICC (Intraclass Correlation Coefficient) were calculated to measure internal consistency among raters and interrater agreement.

resultsWhen analyzing ChatGPT-4 and ASPS patient material, paired t-tests showed a statistically significant increase in overall mEQIP scores for ChatGPT with a mean difference of 7.50 (CI 6.75-8.25; p<0.001). For the mEQIP content-specific scores, a paired t-test revealed a similarly significant increase in ChatGPT scores with a mean difference of 9.75 (CI 9.26-10.24; p<0.001). When evaluating the effect size, a paired Cohen's d value of 13.00 was calculated, demonstrating a statistically significant difference in magnitude between the two groups. To measure internal consistency among raters and interrater agreement, an ICC and Cronbach's alpha were calculated for both ASPS and ChatGPT. The ASPS-mEQIP showed good internal consistency and excellent interrater reliability (ICC=0.89, Cronbach's α=0.84), while ChatGPT-mEQIP showed excellent internal consistency and excellent interrater reliability (ICC=0.96, Cronbach's α=0.96).

conclusionThese results demonstrate that ChatGPT-4-generated patient education on gender-affirming top surgery exceeded the current ASPS online content in both overall and content-specific scores, as measured by the mEQIP tool. ChatGPT achieved significantly higher scores across both domains with large effect sizes, and raters demonstrated excellent internal consistency and excellent interrater reliability. Going forward, commonly accessible artificial Intelligences (AIs), such as ChatGPT, may serve as a valuable complement to patient education and shared decision-making within plastic and reconstructive surgery, though future studies are warranted to evaluate freely generated responses to better reflect current AI use.

Indexed as

artificial intelligence (ai)gender-affirming surgerylarge language modelpatient educationtop surgery

Identifiers

PMID42109970
PMCPMC13152676

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LicenceCC BY
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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.