Evidence map›Paper›PMID 37095384›Full record

Observational studyAesthetic plastic surgery2023

Aesthetic Surgery Advice and Counseling from Artificial Intelligence: A Rhinoplasty Consultation with ChatGPT.

Yi Xie, Ishith Seth, David J Hunter-Smith, Warren M Rozen, Richard Ross, Matthew Lee

Abstract readObservational Study
PubMed Full text at PMC Publisher
In one paragraph

Observational study in Aesthetic plastic surgery, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 70 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
70citing papers in PubMed, 1 pooled it
–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

70 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
  3. Article
  4. Article
  5. Review
  6. Review
  7. Article
  8. Article
  9. Review
  10. Article
  11. Review
  12. Article
  13. Article
  14. Article
  15. Article
  16. Review
  17. Article
  18. Article
  19. Article
  20. Article

10 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

6 authors.

Yi XieDepartment of Plastic Surgery, Peninsula Health, Melbourne, Victoria, 3199, Australia.
Ishith SethDepartment of Plastic Surgery, Peninsula Health, Melbourne, Victoria, 3199, Australia. ishithseth1@gmail.com.ORCID http://orcid.org/0000-0001-5444-8925
David J Hunter-SmithDepartment of Plastic Surgery, Peninsula Health, Melbourne, Victoria, 3199, Australia.
Warren M RozenDepartment of Plastic Surgery, Peninsula Health, Melbourne, Victoria, 3199, Australia.
Richard RossDepartment of Plastic Surgery, Peninsula Health, Melbourne, Victoria, 3199, Australia.
Matthew LeeDepartment of Plastic Surgery, Peninsula Health, Melbourne, Victoria, 3199, Australia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundChatGPT is an open-source artificial large language model that uses deep learning to produce human-like text dialogue. This observational study evaluated the ability of ChatGPT to provide informative and accurate responses to a set of hypothetical questions designed to simulate an initial consultation about rhinoplasty.

methodsNine questions were prompted to ChatGPT on rhinoplasty. The questions were sourced from a checklist published by the American Society of Plastic Surgeons, and the responses were assessed for accessibility, informativeness, and accuracy by Specialist Plastic Surgeons with extensive experience in rhinoplasty.

resultsChatGPT was able to provide coherent and easily comprehensible answers to the questions posed, demonstrating its understanding of natural language in a health-specific context. The responses emphasized the importance of an individualized approach, particularly in aesthetic plastic surgery. However, the study also highlighted ChatGPT's limitations in providing more detailed or personalized advice.

conclusionOverall, the results suggest that ChatGPT has the potential to provide valuable information to patients in a medical context, particularly in situations where patients may be hesitant to seek advice from medical professionals or where access to medical advice is limited. However, further research is needed to determine the scope and limitations of AI language models in this domain and to assess the potential benefits and risks associated with their use. LEVEL OF EVIDENCE V: Observational study under respected authorities. 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

RhinoplastySurgery, PlasticArtificial IntelligenceCounselingEstheticsHumansReferral and ConsultationArtificial intelligenceChatbotChatGPTRhinoplasty

Identifiers

PMID37095384
PMCPMC10581928

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.