Artificial Intelligence (AI)-Assisted Patient Education and Concerns Following Facelift Surgery: A Study on ChatGPT-4 and Gemini.

BACKGROUND

Facelift surgery offers aesthetic results that nonsurgical methods cannot achieve. As an elective procedure, facelift surgery requires patients to have a clear understanding of potential postoperative complications and appropriate postoperative care. Nevertheless, timely access to medical care can be challenging in remote areas. Integrating advanced artificial intelligence (AI) tools such as ChatGPT-4 and Gemini into postoperative care may help bridge this gap by offering continuous patient education and support. This study aimed to evaluate the utility of ChatGPT-4 and Gemini in addressing common patient concerns and education following facelift surgery.

METHODS

The five most frequently asked postoperative facelift questions were submitted to ChatGPT-4 and Gemini. Responses were assessed by five expert facial plastic surgeons based on four criteria: accuracy, clarity, relevance, and response time.

RESULTS

Both AI models demonstrated strong overall performance. ChatGPT-4 and Gemini each achieved an average accuracy of 88%, closely aligning with current medical guidelines. Clarity ratings were 80% for ChatGPT-4 and 92% for Gemini, indicating generally understandable responses. Relevance scores were high, with ChatGPT-4 at 100% and Gemini at 96%, effectively addressing patient concerns. Both models provided instant responses, earning 100% in response time.

CONCLUSIONS

ChatGPT-4 and Gemini show promise as supportive tools in postoperative facelift care. They delivered responses that were accurate, clear, relevant, and timely. While not a replacement for medical professionals, both AI models can serve as valuable adjuncts to patient education, particularly where access to care is limited. These findings highlight AI's emerging role in enhancing postoperative care in facial plastic surgery.

LEVEL OF EVIDENCE IV: 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 .