Description
Artificial intelligence (AI) is also becoming increasingly important in research and technology. It is changing laboratory work at a rapid pace and offers immense opportunities as well as new challenges. In order not to be left behind, laboratory and scientific professionals need a fundamental understanding of AI and its specific applications in the laboratory environment. From sampling, analytics and metrology to reporting, AI can be used to analyze data, identify trends and make predictions.
This seminar lays the foundation for grasping and understanding the topic of AI. You will be given the tools to recognize the potential of AI in your laboratory, make use of it and prepare for future developments.
This seminar is aimed at:
- Laboratory managers and management functions in research, development and quality control who wish to gain a basic understanding of the use of AI in the laboratory environment.
- Quality managers and QA officers who want to take advantage of AI to optimize their laboratory and quality processes.
- IT and LIMS managers who are preparing to implement AI solutions in the laboratory.
After this seminar
- you will understand the basic concepts and terms of artificial intelligence (AI).
- know how Large Language Models (LLMs) work and what is important when using them.
- are familiar with other practical AI tools in addition to Chat-GPT.
- you will have received suggestions on how and for what you can use AI in your laboratory processes to become more efficient and create added value.
- you will understand the perspective of regulatory authorities and be better prepared for future regulatory requirements.
Program
Basic understanding and terms
- What is AI?
- How does AI differ from machine learning (ML)?
- How do AI and ML work?
- Models and areas of application
- Common misunderstandings
Use of Large Language Models (LLM)
- What is a Large Language Model?
- Where is it used?
- Market overview: Chat-GPT and other LLMs
- Which AI systems are already available for the lab?
- Practical applications in the laboratory environment
- In which direction is AI developing?
AI in analytics and measurement technology
- Analysis process from sampling to reporting
- Suitability of sub-processes for the use of AI
- Focus on data: learning to think in terms of data
- AI in laboratory equipment - opportunities, risks and consequences
- Examples: Measurement data evaluation, reporting...
Generating added value from data collections
- Understanding data as the raw material of the future
- Recognizing connections, structures and correlations
- Recognizing and analyzing trends and creating forecasts
- Benefits for process and product optimization
- Developing and selling new data-based products and services
- Examples Preventive maintenance for devices and systems, optimization of maintenance and calibration intervals
Regulatory trends and compliance
- Different regulatory AI approaches at global and international level
- Specific consideration for Germany and Europe
- Interaction of AI with data integrity requirements such as FAIR and ALCOA+
- Examples of inspector statements and trends in government inspections
- Exemplary insights into key aspects of current guidelines
AI in quality management and quality assurance
- Automated creation, updating and management of QM documents
- Optimize and document processes with AI
- AI-supported verification of compliance with quality standards and norms
- More productive internal audits through AI
- Use of AI for error detection and quality assurance
AI projects
- How can AI be successfully implemented in the lab?
- What distinguishes an AI project from a "classic" project?
- The three central roles and their involvement: User, IT, data analyst
- Best practices and success factors from AI projects
- Important for authorities: Compliance with a project standard
- Project examples
You can find the complete program here.
