Evidence map›Paper›PMID 40212094›Full record

ArticlePlastic and reconstructive surgery. Global open2025

Artificial Intelligence Augmentation: Performance of GPT-4 and GPT-3.5 on the Plastic Surgery In-service Examination.

Daniel Najafali, Erik Reiche, Sthefano Araya, Manuel Orellana, Farrah C Liu, Justin M Camacho, Sameer A Patel, Justin M Broyles, Amir H Dorafshar, Shane D Morrison and 2 more

Abstract read
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 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

  1. Article
  2. Large language model applications in facial plastic and reconstructive surgery: a systematic review of applications, performance, and ethical considerations.European archives of oto-rhino-laryngology : official journal of the European Federation of Oto-Rhino-Laryngological Societies (EUFOS) : affiliated with the German Society for Oto-Rhino-Laryngology - Head and Neck Surgery · 2026
    Review
  3. Article
  4. 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

12 authors.

Daniel NajafaliFrom the Carle Illinois College of Medicine, University of Illinois Urbana-Champaign, Urbana, IL.
Erik ReicheDivision of Plastic and Reconstructive Surgery, Brigham and Women's Hospital, Harvard Medical School, Boston, MA.
Sthefano ArayaDivision of Plastic and Reconstructive Surgery, Fox Chase Cancer Center, Temple University Philadelphia, PA.
Manuel OrellanaDepartment of Surgery, Harbor-UCLA Medical Center, Torrance, CA.
Farrah C LiuDivision of Plastic and Reconstructive Surgery, Department of Surgery, Stanford University School of Medicine, Stanford, CA.
Justin M CamachoDivision of Plastic and Reconstructive Surgery, Fox Chase Cancer Center, Temple University Philadelphia, PA.
Sameer A PatelDivision of Plastic and Reconstructive Surgery, Fox Chase Cancer Center, Temple University Philadelphia, PA.
Justin M BroylesDivision of Plastic and Reconstructive Surgery, Brigham and Women's Hospital, Harvard Medical School, Boston, MA.
Amir H DorafsharDivision of Plastic and Reconstructive Surgery, Rush University Medical Center, Chicago, IL.
Shane D MorrisonDivision of Plastic and Reconstructive Surgery, University of Washington at Harborview Medical Center, Seattle, WA.
Leonard KnoedlerDepartment of Plastic, Hand and Reconstructive Surgery, University Hospital Regensburg, Regensburg, Germany.
Paige M FoxDivision of Plastic and Reconstructive Surgery, Department of Surgery, Stanford University School of Medicine, Stanford, CA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: ChatGPT-3.5 scored in the 52nd percentile of the Plastic Surgery In-service Examination, making its knowledge equivalent to a first-year integrated resident. The updated GPT-4 may have improved performance given its more expansive training set. We hypothesized that GPT-4 would outperform its predecessor, making it a more valuable potential asset to surgical education. Methods: Questions from the 2022 Plastic Surgery In-service Examination were given to GPT-4 and GPT-3.5. Both were prompted using 3 different structures. The 2022 American Society of Plastic Surgeons Norm Tables were used to compare the performance of the chatbot to national metrics from plastic surgery residents. Results: GPT-4 answered a total of 237 questions with an overall accuracy of 63% across all 3 strategies. The accuracy was as follows for the prompting schemes: 54% for open ended, 67% for multiple choice (MC), and 68% for MC with explanation. The section with the highest accuracy (74%) among all strategies was Section 4: Breast and Cosmetic. GPT-4's highest scoring methodology (MC with explanation, 68%) placed it in the following national integrated percentiles: 93rd percentile for the first year, 76th percentile for the second year, 52nd percentile for the third year, 34th percentile for the fourth year, 17th percentile for the fifth year, and 15th percentile for the sixth year. GPT-3.5 scored 58% overall. Conclusions: GPT-4 outperformed its predecessor but only scored in the 15th percentile compared with postgraduate year-6 residents. More refinement is needed to achieve performance metrics equivalent to an attending plastic surgeon and become a valuable tool for surgical education.

Identifiers

PMID40212094
PMCPMC11984779

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.