#41 How to use artificial intelligence in pharmacovigilance, part 2 – Niklas Norén

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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 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:

• How to keep up with rapid developments in AI technology
• Why model and performance transparency both matter
• How to protect sensitive patient data when using AI

Want to know more?

Listen to the first part of the interview here.

The CIOMS Working Group XIV published its recommendations for the use of AI in pharmacovigilance in December 2025. For a summary of the report's guiding principles and examples of how they can be applied, check out this article in Drug Safety.

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.

Other recent UMC publications cited in the interview or relevant to the topic include:

• a pre-print on the revised vigiMatch algorithm for duplicate detection
• an article on the pitfalls of disproportionality analysis
• a pre-print on critically appraising AI applications for rare-event recognition

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

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