Signal Detection and Temporal Analysis of Aromatase Inhibitor‐Associated Hepatotoxicity: A Pharmacovigilance Study Integrating Bayesian Belief‐Propagation Network and Frequentist Metrics

Background This study assessed the real‐world hepatotoxicity of third‐generation aromatase inhibitors (AIs) for breast cancer using pharmacovigilance approaches. Methods FAERS data (Q1 2004–Q1 2025) were analysed using a data‐driven disproportionality analysis framework incorporating traditional frequentist metrics and an information‐theoretic Bayesian network. Results A total of 24 liver‐related adverse events and 7 clinical outcomes were extracted in this study. Letrozole associated with the highest number of hepatotoxicity cases and showed the highest BCPNN‐supported reporting signal. Exemestane exhibited the earliest hepatotoxicity onset (median 49 days), significantly earlier ( p = 0.047) than anastrozole (61.5 days) and letrozole (56 days). Conclusion AIs present distinct hepatotoxic profiles. Exemestane requires early vigilance due to its rapid onset, while letrozole exhibits the highest signal. Proactive liver monitoring and individualised management are crucial. Future research should integrate artificial intelligence with multi‐modal real‐world data for predictive risk assessment.