Benchmarking Large Language Model Responses Against Surgical Clinical Practice Guidelines for Chronic Rhinosinusitis: The Importance of User Prompts

Background/Objectives: As patients increasingly rely on large language models (LLMs) for Chronic Rhinosinusitis (CRS) diagnosis, surgical candidacy, and perioperative care, evaluating the accuracy of LLM-generated information against established clinical practice guidelines for surgical management of CRS is essential. Methods: ChatGPT, Google AI, Google Gemini, and Grok were queried using a 21-question guideline-mapped prompt set (long) and a single patient-focused prompt (short). Two physician reviewers independently scored responses using a 3-point rubric across 21 fields. Primary outcomes were guideline-concordant scores; secondary outcomes included readability measured with the Flesch Reading Ease (FRE) and Flesch-Kincaid Grade Level (FKGL). Inter-rater reliability (IRR) was assessed using the intraclass correlation coefficient (ICC). Analyses were performed in SPSSv31. Results: Guideline concordance ranged from 55.36% to 77.98% (p > 0.05), highest for Grok (77.98%, 95% CI 63.67–92.28), followed by Google Gemini (66.67%, 95% CI 35.42–97.91), ChatGPT (55.95%, 95% CI 29.25–82.65), and Google AI (55.36%, 95% CI 29.97–80.75), with Grok significantly outperforming both ChatGPT and Google AI. Prompt structure significantly affected scores. Long-form prompting resulted in higher guideline concordance scores than short-form prompting (+26.19, p < 0.001). The CPG demonstrated a more readable structure, with a higher FRE (44.1), exceeding scores generated by Grok (31.7), ChatGPT (39.9), Gemini (39.2), and Google AI (32.5). In contrast, the CPG was a higher reading grade level (FKGL score of 11.7) than Grok (11.4), Gemini (10.5), and ChatGPT (10.3), but was lower in reading grade compared to Google AI, which produced the highest FKGL score (12.5). IRR was high (ICC = 0.961). Conclusions: LLMs demonstrated similar guideline concordance, suggesting patients can expect comparable accuracy across platforms. While LLMs generally improved FKGL scores compared to the AAO-HNS CPG, they demonstrated lower FRE scores, indicating mixed results on overall readability. However, longer prompt structure meaningfully influenced output quality, highlighting how a user’s ability to frame precise prompts is critical to obtaining accurate information.