Disproportionality analyses are a mainstay of pharmacovigilance research, but without clear guidelines, they often lead to confusion and misinterpretation. Enter the READUS-PV statement: the first-ever guide for reporting disproportionality analyses that are replicable, reliable, and reproducible.

Tune in to find out:

• The history of reporting guidelines in pharmacovigilance and why the READUS-PV guidelines were created
• Why there has been a spike in the publication of disproportionality analyses in recent years and what this means for their reliability
• What it means to publish “good” pharmacovigilance science

Want to know more?

• Read the READUS-PV guidelines, why they were created, and why they are important.
• In 2021, Khouri and colleagues showed that current methods and models used for disproportionality analyses are unreliable, and Mouffak and colleagues found that there is a tendency to overstate results in published disproportionality analyses.
• A book on data mining techniques in Pharmacovigilance by Poluzzi and colleagues delves deeper into this exponential increase in disproportionality analyses.
• This paper elaborates on the Delphi technique, and how it is used to gather data from reviewers to achieve scientific consensus on a problem.

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Read more about Uppsala Monitoring Centre and how we work to advance medicines safety.

Duplicate reports are a big problem when it comes to signal detection, but with the help of machine learning and new ways of comparing reports, we may more effectively detect them.

This episode is part of the Uppsala Reports Long Reads series – the most topical stories from UMC’s pharmacovigilance news site, brought to you in audio format. Find the original article here.

After the read, we speak to author Jim Barrett, Senior Data Scientist at UMC, to learn more about the duplicate detection algorithm and UMC’s work to develop AI resources for pharmacovigilance.

Tune in to find out:

• How the new algorithm handles duplicates in VigiBase
• About different approaches for developing algorithms
• Why it can be challenging to evaluate the performance of an algorithm

Want to know more?

• Listen to the Drug Safety Matters interview with Michael Glaser about his Uppsala Reports article “Ensuring trust in AI/ML when used in pharmacovigilance” and check out the episode’s extensive list of links for more on AI in pharmacovigilance.
• Artificial intelligence in pharmacovigilance – value proposition and the need for critical appraisal, a presentation by Niklas Norén, Head of Research at UMC, given at University of Verona in April 2024.

Finally, don’t forget to ­subscribe to the monthly Uppsala Reports newsletter for free regular updates from the world of pharmacovigilance.

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Read more about Uppsala Monitoring Centre and how we work to advance medicines safety.

Ensuring trust in AI is vital to fully reap the benefits of the technology in pharmacovigilance. Yet, how do we do so while grappling with its ever-growing complexity?

This episode is part of the Uppsala Reports Long Reads series – the most topical stories from UMC’s pharmacovigilance news site, brought to you in audio format. Find the original article here.

After the read, we speak to one of the authors of the article, Michael Glaser, to learn more about how AI and ML has been used in pharmacovigilance so far, and what needs to happen to ensure its continued use in the field.

Tune in to find out:

● How AI and ML are being used today in pharmacovigilance processes

● Why a mindset change is necessary to make full use of AI/ML in pharmacovigilance

● How we may best move forward to implement AI/ML into healthcare.

Want to know more?

To know more about how AI and ML are being used in pharmacovigilance currently, read this scoping review.

To know more about future trends of the use of AI in Biopharma, read this Accenture survey.

• Despite there being major interest in ML and AI to do more than task automation, there are a number of barriers to its implementation in healthcare. Check out this future-focused paper on the use of AI/ML in pharmacovigilance that details how to utilise it to its fullest potential.
• A mindset shift is necessary in terms of how we think about data, in terms of sharing, how to generate data required to effectively train AI/ML models.
• A validation framework must be developed for AI-based pharmacovigilance systems. One suggestion is to do so using a risk-based approach.
• While there is much interest in using recently developed AI technologies such as chatGPT, preliminary studies like this one suggest that the technology has a ways to go to be useful in pharmacovigilance.
• The World Health Organization have published an extensive guideline on the ethics and governance of AI for health.

Join the conversation on social media
Follow us on X, LinkedIn, or Facebook 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 work to advance medicines safety.