Evidence map›Paper›PMID 41214323›Full record

ArticleAesthetic plastic surgery2026

The Utility of ChatGPT for Medical Students on Plastic and Reconstructive Surgery Rotations.

Tricia Mae Raquepo, Mohammed Yamin, Macie Gettings, Shreyas Puducheri, Micaela J Tobin, Samantha Won, Ryan P Cauley

Abstract read
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Article in Aesthetic plastic surgery, 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

7 authors.

Tricia Mae RaquepoDivision of Plastic and Reconstructive Surgery, Department of Surgery Beth Israel Deaconess Medical Center, Harvard Medical School, LMOB 5A, Boston, MA, 02215, USA.
Mohammed Yamin *Division of Plastic and Reconstructive Surgery, Department of Surgery Beth Israel Deaconess Medical Center, Harvard Medical School, LMOB 5A, Boston, MA, 02215, USA.
Macie GettingsDivision of Plastic and Reconstructive Surgery, Department of Surgery Beth Israel Deaconess Medical Center, Harvard Medical School, LMOB 5A, Boston, MA, 02215, USA.
Shreyas PuducheriDivision of Plastic and Reconstructive Surgery, Department of Surgery Beth Israel Deaconess Medical Center, Harvard Medical School, LMOB 5A, Boston, MA, 02215, USA.
Micaela J TobinDivision of Plastic and Reconstructive Surgery, Department of Surgery Beth Israel Deaconess Medical Center, Harvard Medical School, LMOB 5A, Boston, MA, 02215, USA.
Samantha WonDivision of Plastic and Reconstructive Surgery, Department of Surgery Beth Israel Deaconess Medical Center, Harvard Medical School, LMOB 5A, Boston, MA, 02215, USA.
Ryan P CauleyDivision of Plastic and Reconstructive Surgery, Department of Surgery Beth Israel Deaconess Medical Center, Harvard Medical School, LMOB 5A, Boston, MA, 02215, USA. rcauley@bidmc.harvard.edu.ORCID http://orcid.org/0000-0001-8291-433X

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundSubinternships are a key component of the competitive plastic and reconstructive surgery (PRS) match. While preparedness is highly valued, there is no standardized method for students to prepare for surgical cases. ChatGPT, a widely used large language model (LLM), has shown promise in medical education, but its reliability in surgical contexts remains unclear. We aim to evaluate ChatGPT's potential to help students prepare for PRS rotations and subinternships by analyzing its responses to common PRS questions.

methodsA dataset of 267 questions was generated across PRS subtopics. Three procedures were selected per subtopic, each with 12 identical questions categorized by type and format. Each prompt began with, "I am a medical student preparing for my PRS rotation," to contextualize responses. GPT-4o responses were scored for accuracy, completeness, usefulness, relevance, and overall quality with a five-point Likert scale. Low-scoring responses were re-queried using OpenA1 o1.

resultsTwenty-one responses about pre-case reading were excluded due to fabricated articles (average score 1.17). Among the included responses, scores were: accuracy 4.12, completeness 3.88, usefulness 3.96, relevance 4.19, and quality 4.00. Lymphatics scored highest, while head and neck were lowest (p=0.03). Educational questions outperformed surgical questions (p<0.0001). No significant difference was found between question formats. Re-queried responses showed improved scores (p = 0.0001).

conclusionChatGPT performed best in lymphatics and providing educational tools, but had limitations in generating literature and procedural questions. The newer o1 version demonstrated significantly improved performance, suggesting that with continued model refinement, LLMs are poised to become increasingly valuable in surgical education. LEVEL OF EVIDENCE V: 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

Education, Medical, UndergraduatePlastic Surgery ProceduresStudents, MedicalSurgery, PlasticGenerative Artificial IntelligenceHumansLarge Language ModelsArtificial intelligenceChatGPTLarge language modelsMedical educationPlastic surgery

Identifiers

PMID41214323

What OpenQuestion holds

Textmetadata
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.