Description
Artificial intelligence (AI) is increasingly revolutionizing analytics and measurement technology in laboratories. From chemical analysis and biological tests to physical measurement technology and medical diagnostics: AI-supported processes accelerate data processing, increase precision and optimize workflows.
Modern algorithms recognize patterns in complex data sets, evaluate results in real time and provide a basis for decision-making that goes far beyond traditional analysis methods.
Laboratories generate huge amounts of data every day: from R&D projects, from development and approval studies or from routine measurements in quality control. These data sets often include extensive measurement and analysis data that is available in various formats and systems: from modern laboratory information management systems to historical data lakes. Often this data is not used, but only managed.
This seminar teaches how
- AI can be used specifically in various areas of analytics and measurement technology.
- you can exploit the full potential of your laboratory data with the help of artificial intelligence (AI).
- You can gain valuable insights from your data that not only improve laboratory processes, but also create added value for customers, and how you can use them to develop new business models.
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 the seminar
- you will have become familiar with the basic concepts and decision criteria for the use of AI in various analytical methods and measurement technology.
- you will be able to assess which laboratory processes are best suited for the implementation of AI technologies and which are not.
- know specific use cases in which AI is used to improve measurement data evaluation and automate reporting.
- you know how data can be analyzed in order to optimize existing processes and products and thus significantly increase efficiency and quality.
Program
Overview of typical AI applications in analytics and measurement technology (basic lecture by Elmar Harringer)
- Influence of AI in analytics and measurement technology
- Different systems and optimization approaches
- Prospects for laboratories vs. manufacturers of analytics and measurement systems
- Current AI trends
Criteria for the AI suitability of data and data collections (basic lecture by Cornelia Hunke and Jonathan Alles)
- Checking the quality and usability of existing data
- Access options to data collections
- Use process and management data for optimization
- Procedure for identifying data sources to generate synergies
- Recognizing and analyzing trends and creating forecasts
- Understanding and applying the GDPR, data rights and EU AI Act
Fundamentals and practical examples from analytics and measurement technology (user presentation by Jonathan Alles)
- AI in measurement systems
- Increasing accuracy and improving results through AI
- Example of use in the data analysis of laboratory data
- Example of LLM in report generation
- Example from order processing
Cost/benefit and opportunity/risk considerations (learning impulses with discussion)
- Strategies for assessing the use of AI for efficient data management
- AI and big data - mastering and merging data silos
- Expenses and necessary laboratory organization
- Breaking up existing process habits so that modern systems can be used efficiently
Overview of available AI tools for data analysis (online demo Charles Jouanique)
- Presentation of currently available AI tools for laboratory use
- Focus topics, from the perspective of the available tools
- AI tools that labs should start with
- Demonstration of evaluations with a LIMS
Practical examples of creating added value from laboratory data using AI (user presentation by Charles Jouanique)
- Presentation of an application for the evaluation of laboratory data, e.g. preventive maintenance of devices and systems, optimization of maintenance and calibration intervals and much more.
- Automated data analysis with AI tools
- Predictive quality
Options for new data-based business models with examples (in-depth presentation by Charles Jouanique)
- Presentation of enhanced services through data usage with AI
- Opportunities to develop new business areas
- Understanding data as the raw material of the future
Development of relevant use cases with the participants (moderated online whiteboard)
- Discussion round
- 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 lab through AI
- Options for data collection and evaluation
- Tasks of the laboratories for data management
- Internal organization for the optimized use of existing data collections
You can find the complete program here.
