ArticleAesthetic surgery journal. Open forum2024
Comparative Performance of Current Patient-Accessible Artificial Intelligence Large Language Models in the Preoperative Education of Patients in Facial Aesthetic Surgery.
Article in Aesthetic surgery journal. Open forum, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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.
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
3 citing papers in PubMed.
- Utility of Large Language Models for Congenital Microtia Reconstruction Education: Comparison of the Performance of Claude, GPT, and Gemini.Aesthetic plastic surgery · 2026Article
- Effectiveness of large language models in preoperative and discharge education: a systematic review based on an evaluation framework.NPJ digital medicine · 2026Article
- Evaluation of Aesthetic Outcomes Following Botulinum Toxin Treatment Using Multimodal Large Language Models: A Paired Before-and-After Analysis.Aesthetic surgery journal. Open forum · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Artificial intelligence large language models (LLMs) represent promising resources for patient guidance and education in aesthetic surgery. Objectives: The present study directly compares the performance of OpenAI's ChatGPT (San Francisco, CA) with Google's Bard (Mountain View, CA) in this patient-related clinical application. Methods: Standardized questions were generated and posed to ChatGPT and Bard from the perspective of simulated patients interested in facelift, rhinoplasty, and brow lift. Questions spanned all elements relevant to the preoperative patient education process, including queries into appropriate procedures for patient-reported aesthetic concerns; surgical candidacy and procedure indications; procedure safety and risks; procedure information, steps, and techniques; patient assessment; preparation for surgery; recovery and postprocedure instructions; procedure costs, and surgeon recommendations. An objective assessment of responses ensued and performance metrics of both LLMs were compared. Results: ChatGPT scored 8.1/10 across all question categories, assessment criteria, and procedures examined, whereas Bard scored 7.4/10. Overall accuracy of information was scored at 6.7/10 ± 3.5 for ChatGPT and 6.5/10 ± 2.3 for Bard; comprehensiveness was scored as 6.6/10 ± 3.5 vs 6.3/10 ± 2.6; objectivity as 8.2/10 ± 1.0 vs 7.2/10 ± 0.8, safety as 8.8/10 ± 0.4 vs 7.8/10 ± 0.7, communication clarity as 9.3/10 ± 0.6 vs 8.5/10 ± 0.3, and acknowledgment of limitations as 8.9/10 ± 0.2 vs 8.1/10 ± 0.5, respectively. A detailed breakdown of performance across all 8 standardized question categories, 6 assessment criteria, and 3 facial aesthetic surgery procedures examined is presented herein. Conclusions: ChatGPT outperformed Bard in all assessment categories examined, with more accurate, comprehensive, objective, safe, and clear responses provided. Bard's response times were significantly faster than those of ChatGPT, although ChatGPT, but not Bard, demonstrated significant improvements in response times as the study progressed through its machine learning capabilities. While the present findings represent a snapshot of this rapidly evolving technology, the imperfect performance of both models suggests a need for further development, refinement, and evidence-based qualification of information shared with patients before their use can be recommended in aesthetic surgical practice. Level of Evidence 5:
Identifiers
What 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.