This webinar explores how artificial intelligence (AI) is moving from experimentation to operational impact in cell therapy manufacturing, with a focus on machine learning (ML) applications.

Through applied case studies, panelists will show how ML can be used in CAR T and iPSC process development to analyse metabolomics and process data, optimise culture conditions, and improve critical quality attributes.

The session will also highlight how AI-enabled decision-support tools are being integrated into manufacturing workflows to improve process understanding and decision-making, including early signal detection, anomaly identification, and data interpretation within GMP environments.

Attendees will gain insight into practical considerations for implementing AI/ML in cell therapy manufacturing, including regulatory expectations, model trustworthiness, and strategies for integrating AI into existing systems.

Key Learning Objectives:

• Understand how ML can be applied to metabolomics and process data to improve CAR T and iPSC development
• Identify how AI/ML improves process understanding, quality attributes, and decision-making 
• Recognize key considerations for integrating AI into GMP manufacturing environments