AI-enabled risk based monitoring – Cardiac Safety

• Vickas Patel, M.D., Ph.D. – Chief Science and Regulatory Advisor, Cardiac Safety at Clario, part of Thermo Fisher Scientific
• Alain Gay, M.D. – VP, Scientific and Medical Affairs, Cardiac Solutions at Clario, part of Thermo Fisher Scientific

Why high-quality ECG data is essential

High-quality ECG data is essential for accurately evaluating cardiac safety throughout drug development, particularly when assessing risks such as QT interval prolongation, arrhythmias, and other cardiac effects that could impact patient safety or regulatory approval. AI-enabled risk-based monitoring can help sponsors improve ECG data integrity by identifying potential data inconsistencies that might otherwise go undetected.

By analyzing each patient’s unique ECG “fingerprint,” artificial intelligence can detect potential duplicate recordings, patient misidentification, mislabeled ECGs, and unusual longitudinal changes in ECG patterns. These signals can then be reviewed by cardiac safety experts, enabling targeted site retraining, process improvements, and corrective actions that strengthen overall study quality and reliability.

What is the benefit of AI 

AI can enhance risk-based monitoring strategies by providing continuous oversight of ECG data quality throughout a study. Rather than relying solely on traditional retrospective review processes, sponsors can use AI-driven analysis to help identify data quality concerns, reduce errors, and improve patient safety while maintaining regulatory compliance. This approach supports more efficient trial execution by helping clinical teams focus their efforts on the sites, subjects, and datasets that may require intervention, ultimately contributing to more accurate cardiac safety assessments and stronger clinical evidence.

By identifying poor-quality recordings early, sponsors gain actionable insights that allow them to implement corrective measures before quality issues affect later dose cohorts or critical cardiac analyses. Proactive quality assurance approaches help preserve valuable ECG data, reduces the risk of missing or unusable recordings, and increases confidence in future QT and proarrhythmic risk assessments. The result is a smarter, more efficient cardiac safety monitoring strategy that combines advanced analytics with expert clinical review to improve data quality, accelerate decision-making, and support successful drug development programs.