A Laboratory Information Management System (LIMS) provides the central repository for data produced in the laboratory. Mining that data using artificial intelligence (AI) allows managers to make decisions based on insights. But what do we mean by AI? When and how can a laboratory start to incorporate it into its processes, and when will it be all-pervasive in the laboratory?
What is AI?
People misuse the term ‘AI,’ sometimes because they misunderstand what it is, but often because they want to hype up the subject. One definition (from the Oxford English Dictionary) is “the theory and development of computer systems able to perform tasks normally requiring human intelligence, such as visual perception, speech recognition, decision-making, and translation between languages.” But this does not clearly capture what it means for a software program to be ‘intelligent.’ John McCarthy’s 2004 definition is closer to the truth: “It is the science and engineering of making intelligent machines, especially intelligent computer programs….” However, there are more fundamental questions, including what on earth intelligence is and how we will know if a machine displays it?
Can a lump of silicon think the same way as a human? Well, no, maybe not yet at least! But perhaps we are getting closer. Artificial intelligence, as it is often understood, combines computer science and robust datasets to enable problem-solving. This can greatly expand the power of business analytics to find patterns and answers in huge data sets and supports and expands the concepts of machine learning. Deep learning algorithms help to eliminate some of the data preprocessing that has typically been involved with machine learning. These algorithms can ingest and process unstructured data, like text and images, and automate feature extraction, removing some dependency on human experts.
However, actual intelligence is probably more complex than this. It’s about not following rules; it’s not just about translating a language, it’s about knowing when to create a new word where the current language does not quite cut it, and it’s about adapting to the environment you find yourself in. Intelligence is not about using complex mathematical probability to predict what the next word in an article will be and repeating this process until the article is completed. It’s about learning from the dataset, adapting to new inputs, and recognizing patterns in the data.
Whilst true AI is the long-term goal, the common technologies in use today are Machine Learning and Advanced Business Analytics. The term “AI” is often used interchangeably with these concepts. However, we may be closer than we think to a computer passing the Turing Test. This was proposed by Alan Turing, who many
consider the father of modern computing, in his 1950 paper “Computing Machinery and Intelligence,” the essence of which is “Can machines think?”
Data Lakes and Evolution Will Drive AI Adoption
Having robust datasets to work on is key to all current AI solutions. Easy, you might say, because of digitization, laboratories are adopting laboratory information management systems (LIMS). But, along with sample data, laboratories must also keep relevant sample metadata. So, what is metadata, and what do I need to keep? And that is the 10,000-dollar question. Metadata is information that gives sample data context: Where is it from? How was it collected? Is it related to other samples? How is it stored? Who is the custodian? And so on. Different data will have different metadata. For instance, if you are collecting surveillance data from herds of cows to check for bovine spongiform encephalopathy (BSE, or mad cow disease), relevant metadata might include breed, geographic location, ZIP/postcode, herd statistics, related animals, animal feed used, insemination type, and even veterinary case history. If it is a water testing laboratory, then you would be interested in metadata around the sampling point, date and time, location, sample route, sampler, and so forth.
Once we have all this data, we need somewhere to store it so that it is easily accessible. This is where Data Warehouses and Data Lakes come into it. These allow large volumes of data, generally from many different sources, to be brought together. Typically, it is on these large collections of data that the Machine Learning and AI algorithms will run. However, bringing all this data together can reveal another important data issue, that of data compatibility. This can be as simple as ensuring that the data is in a standard format or as complex as ensuring that data items from different systems are identified in the same way. This is an issue that, for example, has plagued the clinical arena, where problems such as multiple identifiers often exist for the same test. This is where data standardization and data standards become important.
Data Analysis
The function of laboratories outside of the R&D area is to analyze samples and report results. The primary output is often in the form of a certificate of analysis or report. It is not, therefore, surprising that many laboratories have not invested in general database analysis functionality for the LIMS. Such tools do exist, though. LIMS suppliers tend to use Power BI or Tableau as their go-to analytics tools. PowerBI is particularly useful as it is ‘free,’ at least at a basic level, for corporate clients who already have Microsoft.
Using Data Analytics tools is one step on the road to AI. You need to ask defined questions to extract the answers you want, however. Show me where clusters of BSE are occurring. Which rivers are the most contaminated in my water samples, and which farmers are applying pesticides nearby? Analytics tools are good at finding trends and outliers in big data sets: an increasing failure rate on certain tests when operator A is in the lab, and instrument Y goes down more often than most instruments of the same type. However, is this enough to answer the real question: why would labs bother investing in Business Analytics, Machine Learning and AI?
Where is AI Gaining Ground?
Accountants often dictate where data analytics is most used because they think they know where there will be a payback on the money invested. In the laboratory field, R&D laboratories, rather than QC laboratories, are more likely to keep and repurpose their data. In the pharmaceutical industry, it is not uncommon for data results from one drug development program to be re-screened against different limits or used in other development programs, for instance. Directing deep learning programs at data lakes within large pharma companies is one area where data analytics and their modern offspring, AI, will pay dividends.
Much, if not everything, depends on the quality of the data, and especially the metadata. The old adage of “rubbish in equals rubbish out” has never been truer. Millions of dollars can be wasted throwing new ideas against a wall to see what sticks, no matter how automated or ‘intelligent’ the data analytics process is, if the data is not up to scratch.
Laboratory Automation and AI Drives Efficiency
Robotic systems are increasingly being used for tasks such as sample handling, liquid dispensing, and data analysis, reducing human error, and increasing throughput. Over time, AI algorithms will assess the overall workload and juggle resource allocation to maximize overall efficiency.
AI could help with predictive maintenance of laboratory equipment, predicting when instruments might fail or require maintenance, ensuring downtime is reduced to a minimum. In a similar way, AI can be used for real-time monitoring and quality control in manufacturing processes, identifying when results start to drift and ensuring tighter process tolerances. Such adaptive learning techniques will help improve bottom-line profitability and are a relatively small step from the more ‘fixed’ process limits we use today.
A Golden Tomorrow
As we have discussed, the typical QC laboratory using a LIMS records results for very specific quality control functions. The strength of alcohol in beer, ensuring food is safe to eat, safeguarding river water quality, checking the purity and value of a precious metal, and so on.
These laboratories see the value of a LIMS for automation and efficiency (going paperless, speeding the reporting function, integrating systems, and so forth). Data Analytics within these laboratories is usually highly directed; help me get my monthly report; show me which tests are most/least profitable for the lab. Who could do with more training? Data Analytics, in the form of Power BI or similar, provides the algorithms required and even a natural search function to help derive that data quickly.
Few non-R&D laboratories, though, are resourced to research their data to learn new ideas from it. Thus, science in this area will move forward more slowly. The vanguards are, however, pharmaceutical companies that are repurposing and rigorously interrogating their data. You can easily imagine, though, that the large datasets such as those of the UK’s NHS would be a rich source of data to help in developing new treatments (say in Parkinson’s disease or Cystic Fibrosis). Proposals such as this do, however, raise issues of data confidentiality and ownership, something that the implementation of AI in various areas has brought to the fore.
The emphasis for most laboratory organizations today should be on improving the quality of their data lake so that it can be used for such purposes in the future. Ensuring your lab uses a LIMS and keeps data digitally prepares it for data analysis, or directed searches, looking for paybacks in efficiency either within the laboratory or across the business operations. Directed searches using data analytics tools will be enough for many, but AI and deep learning are increasingly important within the R&D laboratory, where using data to answer new questions and repurposing past work for future drugs creates a new profit stream for the business. AI will only slowly penetrate the wider laboratory community as AI becomes easier to integrate and will
likely be an extension of the Data Analytics tools you already use as part of your LIMS solution today.
Preparing for the Future of AI in Your Laboratory
As laboratories continue their digital transformation journey, the focus should remain on building a strong data foundation. Implementing a configurable and fully integrated Laboratory Information Management System (LIMS) is the first step toward unlocking the potential of AI and advanced analytics. By ensuring your laboratory data is accurate, structured, and accessible, you set the stage for smarter automation, improved decision-making, and sustainable growth.
Matrix Gemini LIMS offers the flexibility and scalability your laboratory needs to evolve with technology. Its unique configuration capabilities—without custom coding—enable seamless data management, workflow automation, and readiness for future AI-driven advancements.
Transform your laboratory data into actionable insights. Contact us today to learn how Matrix Gemini LIMS can future-proof your laboratory.
