AI Assistant for Laboratories: The Potential of Modern Data Management Systems

Artificial intelligence (AI) opens up promising possibilities for everyday laboratory work. An AI assistant for laboratories can help to increase efficiency and gain new insights into data. While traditional laboratory information and management systems (LIMS) have become indispensable tools since their introduction in the 1970s, they are reaching their limits when it comes to integrating modern AI technologies. Despite the wide range of industry-specific solutions, many laboratories remain dissatisfied with their systems. High costs for device connections, incomplete data storage and time-consuming adjustments to new requirements pose significant challenges.

AI systems can be used in a variety of ways in the laboratory. For example, a digital assistant simplifies research in extensive databases by processing natural language queries and searching for relevant patterns and correlations. This enables complex analyses and visualisations that would be difficult or impossible to recognise without AI support.

An example from quality assurance illustrates the advantages: A plastics processor purchases recycled polymer granules from three suppliers and measures eight parameters such as viscosity, density and particle size distribution in accordance with DIN standards. With ten batches per supplier, this results in 240 measurement points. An AI-supported system can not only display this data clearly, but also automatically identify and visualise anomalies, such as deviations in viscosity for a particular supplier.

The basis for successful AI applications in the laboratory is the complete integration of all relevant data.

Practical Application of AI in the Laboratory

Modern solutions enable seamless connection of various measuring instruments through special mappers that convert data into uniform formats and store them in central databases. Modern solutions enable seamless integration of various measuring instruments through special mappers that convert data into standardised formats and store them in central databases. Linking to existing IT infrastructure such as CRM, MES or other production systems is a prerequisite for efficient access to historical and current data.

A fully integrated system offers several advantages. It enables the rapid comparison of data and automatically highlights values that require special attention. It also provides the basis for the creation of a ‘digital twin’ – a digital copy of a sample that includes recipe data, technical specifications, sample data and measurement results and is continuously updated with real-time data.

Furthermore, AI systems can link lab results with external sources such as scientific publications, patent databases or industry-specific studies. This contextual information enables a deeper understanding of the results and supports the analysis of potential side effects or reactions of certain substances under different conditions.

Another area of application is predicting the performance and quality of new formulations. By analysing historical data and identifying patterns, AI systems can create forecasts that enable proactive quality control and more efficient product development.

The introduction of an AI assistant for laboratories requires a structured approach. A careful analysis of the current situation forms the basis for a sustainable digitalisation strategy. Investigating current data usage, taking into account existing IT systems, leads to a detailed roadmap for implementing laboratory 4.0 solutions, including necessary IT infrastructure upgrades, modernisation of laboratory equipment and adaptation of process workflows.

High data quality is crucial for the successful use of AI in the laboratory. AI systems are only as good as the data they work with. Modern alternatives to traditional LIMS now offer more flexibility and user-friendliness, as well as integrated AI functions. These solutions enable better networking and use of data, which significantly increases efficiency and quality in the laboratory.

Source: Trade journal ‘Laborpraxis’

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