AI (Artificial Intelligence) in a GxP Environment + AI in Visual Inspections - Live Online Training
29-31 October 2024
Course No. 21786
Speakers
Klaus Feuerhelm
Regierungspräsidium Tübingen
Objectives
(AI) Artificial Intelligence in a GxP Environment
Why should you participate in this event?
You will learn the basics of AI / ML and its applicability in the GxP Environment
How can pharmaceutical basics, e.g. risk management and qualification / validation be applied to AI? You will experience first approaches!
Are relevant pharmaceutical regulations adapted to this new technology and what expectations does an inspector have during an inspection? First concepts will be presented!
In case studies, pharmaceutical companies show first practical and practised approaches to the use of AI
(AI) Artificial Intelligence in Visual Inspection
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 conference, we will be focussing on GMP regulation and technical aspects. Questions such as
Does an automatic AI solution eliminate the need for manual visual inspection?
When does the use of Artificial Intelligence make sense?
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 does a project to switch to AI-based visual inspection work?
What GMP authority requirements are there for such systems?
Background
(AI) Artificial Intelligence in a GxP Environment
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 conference, we will be focussing on GMP regulation and technical aspects. Questions such as
Does an automatic AI solution eliminate the need for manual visual inspection?
When does the use of Artificial Intelligence make sense?
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 does a project to switch to AI-based visual inspection work?
What GMP authority requirements are there for such systems?
will be discussed and possible solutions presented.
(AI) Artificial Intelligence in Visual Inspection
At the latest, Artificial Intelligence (AI) has arrived in the general public since ChatGPT and Bard. Opinions range between absolute euphoria and the invocation of the downfall of humanity. The foundations of AI were laid many years ago and can now be widely implemented due to massively available computing power. The topic has also found its way into the pharmaceutical landscape. First applications have come into operation. The interesting questions here are whether and how this technology is compatible with pharmaceutical regulations, specifications and authorities’ expectations.
Target Group
(AI) Artificial Intelligence in a GxP Environment
The Live Online Training is aimed at managers and QA members as well as engineers from the pharmaceutical industry, suppliers and service companies who qualify and operate AI applications in a GxP environment.
(AI) Artificial Intelligence in Visual Inspection
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.
Technical Requirements
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