Artificial Intelligence And Machine Learning In Pharmacovigilance: Unlocking Potential To Drive Innovation In Patient Safety

By Karthik Muthusamy, BMS

Artificial intelligence (AI) and machine learning (ML) hold transformative potential in pharmacovigilance (PV), a field dedicated to monitoring and preventing adverse drug reactions (ADRs). Traditional PV methods are increasingly overwhelmed by the complexity and volume of healthcare data, necessitating innovative solutions. AI/ML technologies promise to enhance the efficiency, accuracy, and timeliness of drug safety monitoring, yet their adoption encounters significant challenges, including data privacy, model bias, and regulatory compliance.

Regulatory bodies like the FDA and EMA are beginning to recognize the value of AI/ML, with efforts underway to develop guidance for their integration into existing frameworks. The application of AI/ML can streamline individual case safety report (ICSR) processing, improve signal detection by analyzing diverse data sources, and facilitate proactive risk management through predictive modeling. A collaborative approach involving regulatory agencies and industry stakeholders is essential to establish effective guidelines that leverage AI's capabilities while complementing the expertise of safety professionals. This comprehensive strategy will enable the realization of AI's transformative impact on pharmacovigilance and drug safety.