Description
Large language models (LLMs) such as ChatGPT and similar AI technologies open up new possibilities for data-driven processes in laboratories. They analyse large text and data sets, provide precise evaluations and support well-founded decisions - from automated documentation to the optimization of complex workflows.
The strengths of LLM are also played out in quality management. AI tools automatically create and update documents such as SOPs or audit reports and support auditors with planning and reporting. QM AI also makes it easier to search through large and/or complex documentation and answers questions without time-consuming research.
The seminar shows in a practical way how AI can be integrated into laboratory processes and QM landscapes: from automated plausibility checks to innovative documentation solutions.
This seminar is aimed at:
- Laboratory managers and management functions in research, development and quality control who wish to acquire a basic understanding of the use of AI in the laboratory environment.
- Quality managers and QA officers who want to use the advantages of AI to optimize their laboratory and quality processes.
- IT, LIMS and data managers who are preparing to implement AI solutions in the laboratory.
After this seminar
- you will have gained a sound understanding of the basic concepts and functions of Large Language Models (LLMs).
- you will be able to recognize and critically evaluate the potential and limitations of LLMs in the laboratory.
- know the basic concepts and advantages of using AI in quality management.
- you are aware of possible application examples or techniques for automating documentation in quality management.
- you can develop strategies on how audits can be planned, carried out and evaluated more efficiently with AI support.
Program
Function, use and evaluation of large language models (basic lecture by Elmar Harringer)
- How an LLM works
- Terms and their meanings
- Deployment in the various laboratory areas, e.g. administration, order processing, sample organization, quality management and data evaluation
- Possibilities for process optimization in the laboratory through AI
- Procedure for assessing the suitability of AI tools for the use case
Practical example of the use of LLMs in the laboratory (user presentation by Charles Jouanique)
- Support for recurring processes in everyday laboratory work
- Laboratory order processing with AI
- Automated report generation through LLM
- Use in the data analysis of laboratory data
- Checking changes to standards for internal documentation requirements
- Customer feedback and complaint processing
Practical examples from QM, document management and auditing (user presentation by Cornelia Hunke)
- Creation, optimization and updating of SOPs using AI tools
- Review of standard, changes to standards for internal documentation requirements
- Real-time monitoring and alerting: detect deviations from standard processes immediately
- Audit preparation through clustering of key topics
- Reduction of errors and waste through early detection to avoid deviations
- Adherence to quality standards - Ensuring compliance
- Supplier qualification, tracking and organizations
- Support in the preparation of inspections by authorities, AI as a sparring partner
Development of relevant use cases with the participants (moderated online whiteboard)
- Exchange with the participants about AI tasks in the laboratory and QM
- Presentation of the task situation from the group of participants
- Solution approaches for implementation with AI tools.
Impulses, perspectives, solutions (Talkshop)
- Changing the way we work in the laboratory through the use of LLMs
- Opportunities and limitations through the use of artificial intelligence
You can find the complete program here.
