The target group for this event are specialists and managers in the pharmaceutical industry from the fields of engineering, production and quality assurance who are involved in the organisation or operation of visual inspection. This conference is also aimed at suppliers involved in the development and automation of inspection systems.
It is the aim of this event to inform about the possibilities and limitations of Artificial Intelligence in the automated visual inspection of parenterals. In addition, Good Machine Learning Practices throughout the entire process will be explained and solutions will be presented on how AI projects can be established and validated in a riskbased and traceable manner in the GMP Environment.
Background
The pharmaceutical industry is increasingly interested in AI for the visual inspection of parenterals to optimize and enhance process efficacy. However, the lack of specific regulatory requirements for AI validation poses challenges from a Good Manufacturing Practice (GMP) perspective, such as data representativeness, model design, and data integrity throughout the product lifecycle.
In visual inspection, AI aims to improve efficiency by reducing the false acceptance rate (FAR) of defect units and the false reject rate (FRR) of good units, which together determine the misclassification rate and the inspection process‘s effectiveness. A high FAR is associated with a possible quality risk, while the FRR is a measure of the economic damage of the selected Control process.
Despite its potential, the FDA‘s guidance on automated Systems mentions AI only briefly, highlighting the need for comprehensive regulation and addressing technical challenges like training, domain knowledge, and data quality. Implementing AI Systems requires specialized expertise, precise data labelling, and Cloud computing for model training.
At this online training, we will be focussing on GMP regulation and technical aspects. Questions such as
- What expectations can be placed on the achievable false reject rates of AI-supported inspection systems?
- Are there applications or technical limitations that even AI-supported systems cannot solve?
- How to set up a project for switching to AI-based visual inspection?
- What GMP authority requirements are there for such systems?
will be discussed and possible solutions presented.