Evidence map›Paper›PMID 40125461›Full record

ArticlePlastic and reconstructive surgery. Global open2025

Assessment of Appearance-related Questions About Breast Reconstruction Generated by Chat Generative Pre-trained Transformer.

Xiomara T Gonzalez, Margaret S Roubaud, Mark V Schaverien, Rene D Largo, Christopher S Parham, Ashleigh M Francis, Tzuan A Chen, Aubri S Hoffman, Ryan M Dickey, Mia K Markey and 1 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Plastic and reconstructive surgery. Global open, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. 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

11 authors.

Xiomara T GonzalezFrom the Department of Electrical and Computer Engineering, The University of Texas at Austin, Austin, TX.
Margaret S RoubaudDepartment of Plastic Surgery, The University of Texas MD Anderson Cancer Center, Houston, TX.
Mark V SchaverienDepartment of Plastic Surgery, The University of Texas MD Anderson Cancer Center, Houston, TX.
Rene D LargoDepartment of Plastic Surgery, The University of Texas MD Anderson Cancer Center, Houston, TX.
Christopher S ParhamDepartment of Plastic Surgery, The University of Texas MD Anderson Cancer Center, Houston, TX.
Ashleigh M FrancisDepartment of Plastic Surgery, The University of Texas MD Anderson Cancer Center, Houston, TX.
Tzuan A ChenDepartment of Psychological, Health, and Learning Sciences, HEALTH Research Institute, University of Houston, Houston, TX.
Aubri S HoffmanDepartment of Biomedical Engineering, The University of Texas at Austin, Austin, TX.
Ryan M DickeyDepartment of Plastic Surgery, The University of Texas MD Anderson Cancer Center, Houston, TX.
Mia K MarkeyDepartment of Biomedical Engineering, The University of Texas at Austin, Austin, TX.
Gregory P ReeceDepartment of Plastic Surgery, The University of Texas MD Anderson Cancer Center, Houston, TX.

Funding

Comprehensive Training Program in Imaging Science and InformaticsT32EB007507 · NIBIB · UNIVERSITY OF TEXAS AT AUSTIN · PI MARKEY, MIA K, RYLANDER, HENRY GRADY · 2009 to 2024
$2.7M
NIBIB NIH HHS T32 EB007507
6 · The paper itself

Abstract

Background: Previous studies have explored the ability of artificial intelligence (AI) tools based on large language models, such as Chat Generative Pre-Trained Transformer (ChatGPT), to answer patient questions about breast reconstruction. In this study, we assessed the quality of questions generated by ChatGPT for breast reconstruction patients to ask their providers. Methods: ChatGPT was prompted to generate appearance-related questions representative of what patients might ask during breast reconstruction consultations. As a benchmark, a comparison group of questions from credible online sources was compiled. Blinded to the source, surgeons assessed the quality of questions in terms of their acceptability, contribution to the informed consent process, and contribution to the shared decision-making process. Surgeons were also asked to report their agreement on whether questions were generated by an AI tool. Results: Experienced reconstructive surgeons rated ChatGPT-generated questions about appearance-related outcomes of breast reconstruction as acceptable (15 of 16), likely to positively contribute to the informed consent process (15 of 16), and likely to positively contribute to the shared decision-making process (16 of 16). These ratings were comparable to those for benchmark questions. Surgeons did not readily recognize questions as being AI-generated. Differences in surgeon assessments were most pronounced regarding the perceived potential for the questions to contribute to the informed consent process. Conclusions: The quality of ChatGPT-generated questions related to appearance concerns is comparable to that of questions sourced from reputable online websites. Patients may benefit from discussion with their providers about best practices for using AI tools in preparation for consultations.

Identifiers

PMID40125461
PMCPMC11927646

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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.