Evaluating AI Responses to Postoperative Questions in Mohs Reconstruction

Patients frequently ask questions after Mohs facial reconstruction. AI tools, particularly large language models (LLMs), may optimize this communication.

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.

Prospective observational study following STROBE guidelines.

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.

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 postoperative communication, but variation in readability and content depth highlights the continued need for physician oversight.