validation of ai-generated toxicology vignettes in singapore: a cross-sectional expert review

Background: generative artificial intelligence (ai) holds promise for medical education, yet the realism and contextual relevance of ai-generated toxicology vignettes in southeast asia are not well established. this study evaluated the face and content validity of vignettes produced by chatgpt-4.0, to assess their plausibility and relevance for use in singaporean emergency medicine education, training, and clinical decision support.methods: ten vignettes were generated using chatgpt-4.0 in march 2025 and independently evaluated by five singapore-based clinical toxicologists from four public hospitals. a six-domain rubric, adapted from established validity frameworks, scored presentation realism, typicality of exposure, toxidrome representation, clinical progression, appropriateness for toxicology consultation, and alignment with local practice. inter-rater reliability was calculated using a two-way random-effects intraclass correlation coefficient [icc (2, k)].results: the mean total score was 20.1/24 (sd = 1.8). inter-rater agreement was excellent (icc = 0.87; 95% ci: 0.80–0.94). face validity averaged 4.4/5 (sd = 0.5) and content validity averaged 4.2/5 (sd = 0.6). most vignettes reflected common regional poisoning patterns, with some depicting rare but plausible exposures relevant to local practice.conclusion: chatgpt-4.0 can generate toxicology vignettes with high expert-rated realism and contextual relevance when tailored to singaporean practice. these findings support its potential role in medical education, simulation, and decision-support tools. further research should compare ai-generated and clinician-authored materials to determine educational impact and applicability in real-world clinical settings.