Concrete Industrial Case Studies of Decision-Making AI
Complimentary
Learning Level: Intermediate
Time: 1000 - 1100 ET
Session Length: 1 hour
Artificial Intelligence and Machine Learning (AI/ML) are transforming data analysis and process optimization across many industries — and their potential in pharmaceutical manufacturing is huge. In this webinar, we will present three real-world industrial use cases from different industries that highlight how AI/ML can drive efficiency, quality, and innovation.
The first use case will be about the use of machine learning for image segmentation for a MedTech Application in the diagnosis of age-related macular degeneration. The second use case will be about the use of machine learning for process assistance to reduce equipment changeover time and improve quality. We will show how an independent assessment of prediction reliability increases model robustness. The third use case will regard the use of machine learning in manufacturing for quality control.
Special emphasis will be placed on tools and methodologies that strengthen model robustness, reliability, and predictive performance — offering insights that can help pharma professionals adapt to their own highly regulated production settings. We will address how a combination of rule-based algorithms to enhance input data quality and machine learning will lead to more robust models.
The work will be contextualized in the framework of the ISPE maturity model and the on-going regulatory changes on the use of AI in pharma, such as the recently published European Annex 22.
Learning Objectives
- Learn from best-practices on the industrial use of Machine Learning from several industries
- Understand how a combination of rule-based algorithms and machine learning leads to more robust data analytics
- Learn approaches for enhancing prediction reliability with ML
