Supplementary file 2_A cross-sectional evaluation of large language model chatbot interfaces for patient-facing herpes zoster information: safety, information quality, and readability.docx

Paired comparisons and inter-rater agreement were evaluated using prespecified statistical methods.</p>Results<p>The five interfaces generated 230 complete responses. Under the study conditions, 66 responses (28.7%) were classified as potentially unsafe using the prespecified ≥3/5-rater majority threshold, with no significant difference between interfaces (Cochran’s Q = 1.661, df = 4, p = 0.798). Sensitivity analyses using alternative thresholds showed consistent results.

Accuracy and empathy differed across interfaces (both p < 0.001; Kendall’s W = 0.127 and 0.211, respectively), although effect sizes were small. Information-quality and overall-quality measures also showed outcome-specific differences. All six readability indices differed across interfaces (all p < 0.001); DeepSeek-V4-Pro generally produced text estimated to be easier to read, whereas Gemini 3.5 Flash produced text estimated to be more difficult to read.

Safety agreement was substantial (Fleiss’ κ = 0.682), and ICC (2,1) values for other manually rated outcomes ranged from 0.832 to 0.889.</p>Conclusion<p>In this standardised benchmark, LLM chatbot interfaces provided generally favourable accuracy and information-quality scores but showed clinically relevant safety limitations, sparse source attribution and disclosure, and readability challenges. No interface consistently performed best across all outcomes.

These findings represent single first responses obtained under the tested conditions and do not establish response stability across repeated queries. LLM-generated herpes zoster information should therefore be interpreted cautiously and should not replace individualised professional assessment or professionally reviewed patient information.</p>