Evaluating AI Responses to Post-Operative Questions in Mohs Reconstruction

Patients frequently ask questions after Mohs facial reconstruction. AI tools, particularly large language models (LLMs), may optimize this communication. Objectives & Hypotheses: We evaluated four LLMs-Claude AI, ChatGPT, Microsoft Copilot, and Google Gemini-on responses to post-operative questions, hypothesizing variation in quality, accuracy, comprehensiveness, and readability. Prospective observational study following STROBE guidelines. 31 common post-operative 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. 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. LLMs show potential for supporting post-operative communication, but variation in readability and content depth highlights the continued need for physician oversight.