Evidence map›Paper›PMID 41233653›Full record

ArticleAesthetic plastic surgery2026

Utility of Large Language Models for Congenital Microtia Reconstruction Education: Comparison of the Performance of Claude, GPT, and Gemini.

Zhifeng Liao, Jiadong Huang, Yukang Liu, Fangwei Li, Li Tang, Liyao Cong, Haibin Wang, Sheng-Kang Luo

Abstract readComparative Study
PubMed Publisher
In one paragraph

Article in Aesthetic plastic surgery, 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

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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

8 authors.

Zhifeng LiaoDepartment of Plastic and Reconstructive Surgery, The Affiliated Guangdong Second Provincial General Hospital of Jinan University, 466 Middle Xin Gang Road, Guangzhou City, 510317, Guangdong Province, China.
Jiadong HuangJinan University-University of Birmingham Joint Institute, Jinan University, Guangzhou City, People's Republic of China.
Yukang LiuThe Second School of Clinical Medicine, Southern Medical University, Guangzhou City, People's Republic of China.
Fangwei LiDepartment of Plastic and Reconstructive Surgery, The Affiliated Guangdong Second Provincial General Hospital of Jinan University, 466 Middle Xin Gang Road, Guangzhou City, 510317, Guangdong Province, China.
Li TangDepartment of Plastic and Reconstructive Surgery, The Affiliated Guangdong Second Provincial General Hospital of Jinan University, 466 Middle Xin Gang Road, Guangzhou City, 510317, Guangdong Province, China.
Liyao CongDepartment of Plastic and Reconstructive Surgery, The Affiliated Guangdong Second Provincial General Hospital of Jinan University, 466 Middle Xin Gang Road, Guangzhou City, 510317, Guangdong Province, China.
Haibin WangDepartment of Plastic and Reconstructive Surgery, The Affiliated Guangdong Second Provincial General Hospital of Jinan University, 466 Middle Xin Gang Road, Guangzhou City, 510317, Guangdong Province, China.
Sheng-Kang LuoDepartment of Plastic and Reconstructive Surgery, The Affiliated Guangdong Second Provincial General Hospital of Jinan University, 466 Middle Xin Gang Road, Guangzhou City, 510317, Guangdong Province, China. luoshk@gd2h.org.cn.ORCID http://orcid.org/0000-0002-5123-1656

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundChildren with microtia and their parents require comprehensive information to make informed decisions about treatment options.

objectiveWe evaluated the effectiveness of various large language models (LLMs) in providing preoperative education for congenital microtia reconstruction (CMR) by analyzing their responses to related inquiries.

methodsTen plastic surgeons developed 13 CMR-related preoperative education strategies and input 14 text commands into Claude-3-Opus, GPT-4-Turbo, and Gemini-1.5-Pro during an online session. Five experts evaluated these language model's responses for correctness, completeness, logic, and potential harm, while five postoperative patients' parent reviewed the education materials for readability and value. All responses were also analyzed for readability using the context package.

resultsThe results showed no statistically significant differences among Gemini, Claude, and GPT in the evaluation metrics of accuracy, completeness, and potential risk. In terms of logicality and overall rating, Gemini's responses were significantly superior to GPT. Preoperative patient education materials generated by GPT received the highest DISCERN scores, significantly outperforming those from Claude and Gemini. From the perspective of patient's parent's, there are no statistically significant differences among Gemini, Claude, and GPT. Objective assessments of readability confirmed that Claude's materials were easier to understand compared to those from the other models.

conclusionClaude-3-Opus, GPT-4-Turbo, and Gemini-1.5-Pro effectively addressed patient inquiries and produced clear pre-surgical education materials. However, these LLMs should not be used independently for patient education without expert supervision to ensure accuracy and completeness. LEVEL OF EVIDENCE IV: This journal requires that authors assign a level of evidence to each article. For a full description of these Evidence-Based Medicine ratings, please refer to the Table of Contents or the online Instructions to Authors www.springer.com/00266 .

Indexed as

Congenital MicrotiaLarge Language ModelsPatient Education as TopicPlastic Surgery ProceduresChildFemaleGenerative Artificial IntelligenceHumansMaleArtificial intelligenceChatGPTLarge language modelsLLMsMicrotiaOnline medical consultationPatients education

Identifiers

PMID41233653

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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.