Evidence map›Paper›PMID 39697940›Full record

ArticleCureus2024

Assessing the Quality, Readability, and Acceptability of AI-Generated Information in Plastic and Aesthetic Surgery.

Muireann Keating, Stephanie M Bollard, Shirley Potter

Abstract read
In one paragraph

Article in Cureus, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing 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

5 citing papers in PubMed.

  1. 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
  2. Article
  3. Article
  4. Can ChatGPT pass the Turkish Orthopedics and Traumatology Board Examination? Turkish orthopedic surgeons versus artificial intelligence.Ulusal travma ve acil cerrahi dergisi = Turkish journal of trauma & emergency surgery : TJTES · 2025
    Observational
  5. 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

3 authors.

Muireann KeatingDepartment of Plastic and Reconstructive Surgery, St James's Hospital, Dublin, IRL.
Stephanie M BollardSchool of Medicine, University College Dublin, Dublin, IRL.
Shirley PotterPlastic and Reconstructive Surgery, Mater Misericordiae University Hospital, Dublin, IRL.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

introductionWithin plastic surgery, a patient's most commonly used first point of information before consulting a surgeon is the internet. Free-to-use artificial intelligence (AI) websites like ChatGPT (Generative Pre-trained Transformers) are attractive applications for patient information due to their ability to instantaneously answer almost any query. Although relatively new, ChatGPT is now one of the most popular artificial intelligence conversational software tools. The aim of this study was to evaluate the quality and readability of information given by ChatGPT-4 on key areas in plastic and reconstructive surgery.

methodsThe ten plastic and aesthetic surgery topics with the highest worldwide search volume in the 15 years were identified. These were rephrased into question format to create nine individual questions. These questions were then input into ChatGPT-4. The response quality was assessed using the DISCERN. The readability and grade reading level of the responses were calculated using the Flesch-Kincaid Reading Ease Index and Coleman-Liau Index. Twelve physicians working in a plastic and reconstructive surgery unit were asked to rate the clarity and accuracy of the answers on a scale of 1-10 and state 'yes or no' if they would share the generated response with a patient.

resultsAll answers were scored as poor or very poor according to the DISCERN tool. The mean DISCERN score for all questions was 34. The responses also scored low in readability and understandability. The mean FKRE index was 33.6, and the CL index was 15.6. Clinicians working in plastics and reconstructive surgery rated the questions well in clarity and accuracy. The mean clarity score was 7.38, and the accuracy score was 7.4.

conclusionThis study found that according to validated quality assessment tools, ChatGPT-4 produced low-quality information when asked about popular queries relating to plastic and aesthetic surgery. Furthermore, the information produced was pitched at a high reading level. However, the responses were still rated well in clarity and accuracy, according to clinicians working in plastic surgery. Although improvements need to be made, this study suggests that language models such as ChatGPT could be a useful starting point when developing written health information. With the expansion of AI, improvements in content quality are anticipated.

Indexed as

aesthetic surgeryartificial intelligencechatbotmedical technologyplastic surgery

Identifiers

PMID39697940
PMCPMC11652792

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

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.