Implementing AI systems

This AI-generated conversation explores the challenges of implementing AI in pharma accurately, reliably and safely, particularly given the stringent requirements of good practice (GxP) regulations and the need to enhance trust among stakeholders, including regulatory authorities, healthcare professionals, and patients. 

It is based on the BioPhorum publication Implementing AI systems in regulated pharma environments and key takeaways include: 

1. The whole AI lifecycle needs to be managed 
2. It's a risk-based approach with risk management throughout 
3. Setting clear objectives and scope creates the essential focus for success 
4. For reliable AI, data needs to be really well managed 
5. Algorithm selection and model design needs discipline to create assurance 
6. Deployment is about thinking of the whole system, which could include many AIs 
7. Data drifts, so ongoing monitoring and evaluation is essential 
8. Change control includes a much bigger challenge of version management 

You can also watch a 90-min webinar replay by nine of the (real-life) authors who illustrate each point from their experience.