An evaluation of breast augmentation: accuracy, utility, and accessibility of knowledge for patient education and consultation with artificial intelligence

Abstract

BackgroundThe purpose of this study is to compare the answers of the 12 most frequently asked questions regarding breast augmentation surgery on both Google Gemini and ChatGPT-4.5. Our research highlights the growing importance and differential performance of artificial intelligence models in informing patients before surgery.MethodsThe 12 most asked questions about breast augmentation, based on user engagement metrics, were obtained from the Realself website. These twelve questions were investigated on both Google Gemini and ChatGPT-4.5. Information received from both platforms was analyzed and evaluated by ten plastic surgeons with European Board of Plastic Reconstructive and Aesthetic Surgery (EBOPRAS) certification. The surgeons, who reached a consensus on the application of the Global Quality Score (GQS) scale prior to evaluation, were blinded to the source of the answers. The GQS scale was used for the evaluation.ResultsThe average results obtained were compared with each other. While the average of Google Gemini responses was calculated at 2.842, the average of ChatGPT-4.5 responses was calculated at 3.867. For this calculation, the Wilcoxon signed-rank test was used. It was found that the ChatGPT-4.5 responses were statistically superior to Google Gemini according to the Global Quality Score (GQS) (p = 0.003).ConclusionWhile there is emerging research on AI in plastic surgery, studies specifically comparing ChatGPT and Gemini for breast augmentation patient education using a blinded evaluation by board-certified surgeons remain limited. We suggest that AI-powered chatbots offer significant advantages for patient education but should be used cautiously. While ethical concerns persist, this study underscores the practicality of ChatGPT in informing patients about plastic surgery procedures, emphasizing the need for careful usage and collaboration to optimize benefits while minimizing risks.Level of Evidence VThis 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.