Jazmín Aguado‐Sierra

The paper uses AI-enhanced cardiac digital twins to assess drug proarrhythmic risk by propagating experimental uncertainty and simulating extreme exposures, finding sex-specific arrhythmogenic thresholds for loperamide.

This study uses computational models to assess drug-induced cardiac risk in vulnerable populations, showing greater QT prolongation and arrhythmia risk in specific heart conditions and females.

This paper develops sex-specific cardiac emulators to predict drug-induced QT interval prolongation in real-time, achieving high accuracy and drastically reducing computational time for drug safety assessments.