A reproducible statistical evaluation framework for large-sample assessment of AI-generated medical references: cross-platform application of the reference hallucination score

OBJECTIVE: To quantitatively evaluate the bibliographic reliability of AI-generated medical references across multiple chatbot platforms using the Reference Hallucination Score (RHS) and to examine the influence of output format on reference stability. METHODS: In this cross-sectional comparative study, three AI-based chatbots (ChatGPT, Gemini, and Perplexity) were prompted under 30 predefined medical subheadings in two output formats (letter and review article), generating 3,150 references. Reference reliability was assessed using the RHS, which evaluates presence/verifiability, bibliographic accuracy, PMID validity, and topic relevance. Due to non-normal distribution, data were analyzed using Kruskal-Wallis and Mann-Whitney U tests with Bonferroni correction. Effect sizes (η² and r) were calculated to distinguish statistical detectability from practical magnitude. RESULTS: Significant inter-model differences were detected in total RHS scores (H(2) = 24.88, p < 0.001); however, the overall effect size was very small (η² = 0.007). Pairwise comparisons revealed statistically detectable differences between certain models, although effect sizes were consistently small (r = 0.02-0.20). Format-related differences were observed, with longer outputs demonstrating reduced bibliographic stability; however, these differences were numerically modest and partially attenuated after Bonferroni correction. CONCLUSION: AI-based chatbots exhibit measurable bibliographic instability in reference generation, with statistically detectable but practically small differences between models. Beyond cross-platform comparison, this study proposes an instrument-agnostic statistical framework for large-sample evaluation of AI-generated reference reliability. Positioned within applied methodological research rather than AI benchmarking, the framework may serve as a template for future large-scale AI reliability studies. The proposed framework may contribute to methodological standardization in future evaluations of AI-generated scientific outputs.