Far from a future add-on, artificial intelligence is already embedded in the cycle of drug safety, from case processing to signal detection. Versatile generative AI models have raised the bar of possibilities, but they have also increased the stakes. How do we use them without losing trust and where do we set the limits?

In this two-part episode, Niklas Norén, head of Research at Uppsala Monitoring Centre, unpacks how artificial intelligence can add value to pharmacovigilance and where it should – or shouldn’t – go next.

Tune in to find out:

• Why pharmacovigilance needs specific AI guidelines
• How a risk-based approach to AI regulation works
• Where in the PV cycle is human oversight most needed

Want to know more?

In May 2025, the CIOMS Working Group XIV drafted guidelines for the use of AI in pharmacovigilance. The draft report received more than a thousand comments during public consultation and is now being finalised.

Earlier this year, the World Health Organization issued guidance on large multi-modal models – a type of generative AI – when used in healthcare.

Niklas has spoken extensively on the potential and risks of AI in pharmacovigilance, including in this presentation at the University of Verona and in this Uppsala Reports article. His favourite definition of AI remains the one proposed by Jeffrey Aronson in Drug Safety.

For more on maintaining trust in AI, revisit this interview with GSK’s Michael Glaser from the Drug Safety Matters archive.

The AI methods developed by UMC and cited in the interview include: 

• vigiMatch for duplicate detection
• vigiGroup for clustering
• vigiRank for signal detection
• vigiGrade for case report completeness scoring
• WHODrug Koda for automated coding

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About UMC
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