ArticleAesthetic plastic surgery2025
Advancement of Generative Pre-trained Transformer Chatbots in Answering Clinical Questions in the Practical Rhinoplasty Guideline.
Article in Aesthetic plastic surgery, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
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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.
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.
Who cites it
2 citing papers in PubMed.
- Qualitative and Quantitative Assessment of Four Artificial Intelligence Systems for Nasal Deformity Analysis in Rhinoplasty.Aesthetic plastic surgery · 2026Article
- Gender and racial diversity Assumed by text-to-image generators in microsurgery and plastic surgery-related subspecialities.Journal of hand and microsurgery · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThe Generative Pre-trained Transformer (GPT) series, which includes ChatGPT, is an artificial large language model that provides human-like text dialogue. This study aimed to evaluate the performance of artificial intelligence chatbots in answering clinical questions based on practical rhinoplasty guidelines.
methodsClinical questions (CQs) developed from the guidelines were used as question sources. For each question, we asked GPT-4 and GPT-3.5 (ChatGPT), developed by OpenAI, to provide answers for the CQs, Policy Level, Aggregate Evidence Quality, Level of Confidence in Evidence, and References. We compared the performance of the two types of artificial intelligence (AI) chatbots.
resultsA total of 10 questions were included in the final analysis, and the AI chatbots correctly answered 90.0% of these. GPT-4 demonstrated a lower accuracy rate than GPT-3.5 in answering CQs, although without statistically significant difference (86.0% vs. 94.0%; p = 0.05), whereas GPT-4 showed significantly higher accuracy for the level of confidence in Evidence than GPT-3.5 (52.0% vs. 28.0%; p < 0.01). No statistical differences were observed in Policy Level, Aggregate Evidence Quality, and Reference Match. In addition, GPT-4 rated significantly higher in presenting existing references than GPT-3.5 (36.9% vs. 24.1%; p = 0.01).
conclusionsThe overall performance of GPT-4 was similar to that of GPT-3.5. However, GPT-4 provided existing references at a higher rate than GPT-3.5. GPT-4 has the potential to provide a more accurate reference in professional fields, including rhinoplasty. 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
Identifiers
39322837What OpenQuestion holds
Registered trials
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.