Decoding Mohs Micrographic Surgery Through an Artificial Intelligence-Powered Search Analytics Approach

Background Patients often seek information online regarding Mohs micrographic surgery. Google's "People Also Ask" algorithm provides insights into patients' questions, compiling frequent queries through machine learning. The experimenters aimed to utilize modern search analytics methods with the integration of machine learning qualitative data analysis to determine patient concerns and questions surrounding Mohs surgery. Methodology Rothwell's classification system and the Journal of the American Medical Association benchmark criteria were used to evaluate question-website pairs generated by Google's "People Also Ask" function. Statistical analyses assessed machine learning classification performance and relationships between search terms, question types, and website categories. Results The highest proportion of questions belonged to technical details (19.8%), follow-up care (12.5%), and indications (11.6%). Among website types, government sites had the highest Journal of the American Medical Association benchmark scores. Compared with human reviewers, machine learning algorithms exhibited kappa values as high as 0.86. Conclusions Mohs surgery patients are interested in understanding the technical details of their condition and procedure. Healthcare professionals should address these concerns to ensure greater compliance with treatment protocol and mitigate the risk of iatrogenic trauma. In qualitative research applications involving a panel of reviewers, high-performing large language models offer a suitable component of a review board and complement human expertise.