Lab Technologies Embracing AI in the Lab of the Future

In the lab of the future researchers will be freed from manual, repetitive tasks allowing them to focus on interpreting results and answering important scientific questions. The term “lab of the future” refers to the digitally connected and seamlessly integrated technologies spearheading this revolution. At the heart of a lot of this technology lies artificial intelligence (AI), the simulation of human intelligence processes by machines, and machine learning (ML), a subset of AI that uses algorithms that are “trained” to recognize patterns and themes in datasets. of corporate researchers say the impact of AI in their area will be transformative or significant. Leveraging innovative AI and ML solutions will open new doors in data analysis, image processing and lab monitoring. This infographic outlines a range of lab technologies that will benefit from incorporating AI. 71% DATA MANAGEMENT & ANALYSIS AI-DRIVEN TECHNOLOGIES IN THE LAB Lab data output has increased exponentially as experiments have become more complex and instruments more advanced. AI can help researchers analyze and draw conclusions from these large datasets, while machine learning can recognize patterns present in datasets and make predictions based on this. A LIMS enables a lab to track data associated with samples, experiments, lab workflows and instruments. Incorporating AI into these systems allows lab managers to mine this data and make informed decisions based on prescriptive analytics. These insights range from resource management tips to maximize efficiency to providing predictive maintenance of laboratory equipment. Open-source lab software solutions use source code that is freely available to download, modify and run. Open-source AI software has been developed for several lab applications. For example, a research paper published in Nature Catalysis introduced RENAISSANCE, an AI-based tool combining various types of cellular data to accurately depict metabolic states. The model’s code is publicly available on GitHub, a cloud-based code repository. AI when used with sensors and LIMS systems can help monitor laboratory environments in real-time, for example, to detect unusual temperature variations within ultralow-temperature freezers. Predictive analytics approaches have found use in technologies such as HPLC, where researchers have used AI models to make predictions on the qualitative properties of an anti-Alzheimer agent, saving time and resources that would usually be spent in method development. LABORATORY INFORMATION MANAGEMENT SYSTEMS (LIMS) REAL-TIME DATA MONITORING OPEN-SOURCE AI SOFTWARE Image processing and analysis can be a laborious and complex task for researchers. AI can streamline this process with applications spanning live cell analysis to diagnostics. Deep learning models can be used to train artificial neural networks to analyze microscopy images by learning patterns and structures from datasets. This enables the models to perform tasks such as image segmentation, object detection and classification with increased precision. AI-powered analysis of medical images can enhance diagnostic accuracy and enable clinicians to extract additional information from samples. Real-time live cell analysis enables the capture of biological changes as they occur, offering unique insights essential to our understanding of disease. However, analyzing and interpreting the large datasets acquired by live cell analysis can be challenging. AIdriven analysis can streamline this workflow and recognize patterns in data that the human eye misses. Researchers have combined X-ray microcomputed tomography (microCT) with machine learning segmentation to digitally deconstruct leaves in 3D. The method dramatically reduced the time required to process single-leaf microCT scans into detailed segmentations. Boston University researchers created an AI tool that combines commonly collected patient data with neuroimaging data to aid with the diagnosis of dementia. The AI model was used to determine what was causing a person’s cognitive decline resulting in a 26% increase in the accuracy of a doctor’s diagnosis. NHS England has set up the NHS AI lab to develop, train and test AI products for healthcare. This endeavor includes the development of the National Medical Imaging Platform (NMIP), a centralized UK database of medical images to support patient diagnostics. A paper published in Nucleus described a compendium of AI-based strategies to analyze the spatial distribution of nuclear and chromosomal signals from 3D image stacks acquired from microscopy. MICROSCPOPY MEDICAL IMAGING LIVE CELL ANALYSIS IMAGING & DIAGNOSTICS SEQUENCING TECHNOLOGY Genomics is a natural candidate for AI and ML because of the sheer size of the datasets involved. Whole-genome sequencing analysis is typically complicated with numerous steps required to identify genetic variants in a human genome. AI software has now been developed that can predict disease-causing genetic mutations. Researchers have created a large language model called Geneformer. This model takes data on gene interactions from a broad range of human tissues and transfers this knowledge to predict how disruptions to these networks may influence disease. DRUG DISCOVERY Identifying promising drug targets and designing new drug compounds. Identifying existing drugs with the potential to be repurposed for other diseases. Predicting the absorption and passage of a drug through the body in preclinical models. Tools powered by AI have the potential to revolutionize drug discovery, particularly in the screening of millions of potential target compounds. The potential applications of AI-powered tools in drug discovery are vast and include: ROBOTIC AUTOMATION AI and ML are being incorporated into robotic systems to improve how these robotics interact with different environments. Using ML methods, such as supervised learning, reinforcement learning and generative AI, robots can be taught to perform unique tasks within laboratory experiments. AI-driven liquid handlers can adapt protocols based on historical data and real-time feedback, optimizing parameters for enhanced efficiency and accuracy. A “self-driving” laboratory of robotic equipment powered by a simple machine learning model has successfully reengineered enzymes to be more tolerable to heat without human input save for hardware fixes. THE FUTURE OF AI IN LAB TECHNOLOGIES Continuous improvement in AI models has led to the emergence of unique tools for combatting a range of laboratory challenges. Organizations such as UK Research and Innovation (UKRI) continue to invest in new AI research hubs, transforming the way labs utilize AI. Sponsored by Sentiments around AI use in the lab are positive, as highlighted by Elsevier’s “Insights 2024: Attitudes toward AI” report. Despite this, several concerns, such as the need for skilled personnel to manage AI systems and questions on how to best integrate AI with existing lab infrastructure, still need to be addressed before AI-powered labs of the future become commonplace. of life sciences companies surveyed by the nonprofit Pistoia Alliance in 2023 said that AI/ML will be their top technology investment over the next two years. 60%