Evaluating AI Responses to Postoperative Questions in Mohs Reconstruction.
Journal:
Facial plastic surgery : FPS
Published Date:
Aug 25, 2025
Abstract
INTRODUCTION: Patients frequently ask questions after Mohs facial reconstruction. AI tools, particularly large language models (LLMs), may optimize this communication. OBJECTIVES AND HYPOTHESES: We evaluated four LLMs-Claude AI, ChatGPT, Microsoft Copilot, and Google Gemini-on responses to postoperative questions, hypothesizing variation in quality, accuracy, comprehensiveness, and readability. STUDY DESIGN: Prospective observational study following STROBE guidelines. METHODS: A total of 31 common postoperative questions were created. Each was submitted to all four LLMs using a standardized prompt. Responses were evaluated by blinded facial plastic surgeons using validated scoring tools (EQIP, Likert scales, readability formulas). IRB exemption was granted. RESULTS: Claude AI outperformed others in quality (EQIP: 90.3), accuracy (4.55/5), and comprehensiveness (4.60/5). All LLMs exceeded the recommended 6th-grade reading level. CONCLUSION: LLMs show potential for supporting postoperative communication, but variation in readability and content depth highlights the continued need for physician oversight.
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