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When you hear the words Alzheimer's disease, what do you think of? The truth is, the picture most of us have of the disease is incomplete. Alzheimer's disease doesn't start when someone starts to lose their memory. It actually starts years – sometimes decades – earlier. The Rethinking Alzheimer's Disease Podcast is an engaging, narrative-style podcast miniseries for those curious or motivated to learn about Alzheimer’s disease. Perhaps you have a family member with Alzheimer’s disease, or ca ...
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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.
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.
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.
Join the conversation on social media
Follow us on Facebook, LinkedIn, X, or Bluesky and share your thoughts about the show with the hashtag #DrugSafetyMatters.
Got a story to share?
We’re always looking for new content and interesting people to interview. If you have a great idea for a show, get in touch!
About UMC
Read more about Uppsala Monitoring Centre and how we promote safer use of medicines and vaccines for everyone everywhere.
Chapters
1. Welcome & introduction (00:00:00)
2. The 'black box' issue and transparency (00:01:43)
3. Managing fast-changing models (00:07:18)
4. Future-proofing guideline principles (00:10:37)
5. Data privacy and prompt risks (00:13:00)
6. Bias in safety data: risks and opportunities (00:15:34)
7. Avoiding hype and fear (00:17:42)
8. Hallucinations and persuasive outputs (00:20:18)
9. Evolving roles and new skills (00:22:12)
10. What’s next for CIOMS guidelines (00:25:25)
11. Closing, resources and credits (00:27:40)
58 episodes
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