Artificial intelligence is creating a new opportunity for pharmaceutical companies to make pharmacovigilance more proactive, turning growing volumes of real-world evidence into faster insight and stronger decision support.

Medicines are now monitored across a far broader evidence landscape than the traditional Individual Case Safety Report (ICSR). Scientific literature, clinical studies, registries, electronic health records, claims data and other real-world evidence can provide valuable insight into how medicines perform beyond clinical trials.

The opportunity for pharma is to connect that information more effectively.

AI and machine learning can help safety teams analyse large volumes of data, identify patterns and prioritise areas for further investigation. This could allow specialists to move more quickly from data collection towards scientific assessment, creating greater value from existing pharmacovigilance resources.

The European Medicines Agency is already exploring this potential. Its artificial intelligence programme identifies AI as an important tool for leveraging large volumes of regulatory and health data, supporting research, innovation and regulatory decision-making across the medicine lifecycle. Its 2026–2028 work plan also includes developing AI guidance for clinical development and pharmacovigilance.

For pharmaceutical companies, this signals an important shift. AI is increasingly becoming an additional analytical layer that can support highly trained pharmacovigilance professionals, helping them spend less time on repetitive data processing and more time interpreting evidence and making informed decisions.

Real-world evidence is also expanding rapidly. The EMA’s DARWIN EU network now connects approximately 40 data partners and provides access to healthcare data representing around 250 million patients across Europe. Its studies support understanding medicine use, safety and effectiveness throughout the lifecycle.

This creates a significant opportunity for pharma organisations to strengthen how they learn from medicines after launch.

The FDA is developing similar capabilities. Its Sentinel Initiative uses large-scale real-world data for post-market safety monitoring, while its Innovation Center is exploring artificial intelligence, natural language processing and machine learning to improve access to and analysis of healthcare information.

Together, these developments point towards a more connected model of pharmacovigilance, where advanced analytics can help teams identify relevant evidence, connect information across sources and bring emerging questions to expert attention sooner.

The commercial opportunity extends beyond efficiency. More responsive safety intelligence can support lifecycle management, inform regulatory assessments, strengthen safety communications and help pharmaceutical companies continue learning from real-world medicine use.

For pharma leaders, the opportunity is to combine technology with specialist expertise and robust governance. With the right approach, AI can help transform pharmacovigilance from a predominantly data-intensive function into a more proactive source of scientific and strategic insight.

The result could be a smarter, more connected approach to medicine safety, helping pharmaceutical companies turn increasingly complex evidence into better-informed decisions throughout the medicine lifecycle.