Drug Safety Matters

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.

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