Laboratory Information Management Systems (LIMS) have evolved over time, moving from software used to capture data in the laboratory space to one used for instrument integration and interfacing to connect to other software systems, such as Enterprise Resource Planning (ERP), SAS, and several other quality systems. Each advancement has resulted in more data creation, higher efficiency, and greater throughput introduced into LIMS. In recent years, we’ve seen yet another technological wave, as companies begin to leverage machine learning (ML), artificial intelligence (AI), and advanced analytics in LIMS.  

Today, LIMS vendors are partnering with companies that specialize in AI to bring the advancements of technology into the laboratory, including automated review and approval processes with a by-exception approach, predictive instrument maintenance to ensure lower failure rates, and predictive inventory use to alert reordering keeping the lab running smoothly. 

ML, AI, and LIMS  

With the ever-increasing complexity of laboratory testing and rapid growth of data in the laboratory, technology in LIMS is evolving to encompass Machine Learning (ML) and Artificial Intelligence (AI). New technologies in LIMS are going beyond searching for data using Structured Query Language (SQL) and instead searching the semantic layer for images, such as chemical structures, leveraging capabilities that mimic ChatGPT to not only search the internet but also search the internal organization’s documentation within the LIMS system. This enables the LIMS system to find what’s needed in a matter of seconds, giving analysts and researchers more time to spend on research and testing procedures and less time searching.  

Advanced Analytics: Enabling the Lab of the Very Near Future 

AI capabilities within LIMS also bring opportunities for advanced analytics. Historically, analytics could show only a descriptive view of the data and analyze the already completed data, and rapid data makes this analysis difficult to manage efficiently and quickly. Adding the layer of advanced analytics enabled by AI allows for a view into a deeper level of predictive analytics, which estimates what could happen with the data trends. It also can provide insight into prescriptive analytics on what the analyst or business should do to remediate the potential from the predictive output.  

Advanced analytics modules have the ability to predictively pass or fail results, perform advanced statistical analysis, analyze calibration curves, and provide insight into the data with an immediate and seamless response. The semantic layer – the data layer that LIMS uses – is a layer of data the business and end users can access and analyze to give meaning to the data. This layer maps different data definitions from different data sources and provides a representation of data in a single view.   

Using a module addition for advanced analytics in LIMS (instead of a third-party application) can also aid in maintaining data integrity and ALCOA+ features that are already showcased in a LIMS solution. Interactive dashboards, charts, and graphs allow deeper views into the data with a click of a button, all while the module is recognizing data patterns, predicting, and recommending steps for preventing delays or interruptions to daily processes. The Lab of the Very Near Future encompasses all these features and can make an organization more efficient and effective.  

If you’re looking to prepare or enable your lab of the future, connect with one of our LIMS experts today.