Whether running a simple blood test or analyzing the effects of a potential treatment on a cell culture, some of the most important answers in health and life sciences come from the lab. A lab thrives on high accuracy, fast speeds, and high throughput. The more efficiently a lab runs, the faster researchers can make discoveries and clinicians can make diagnoses, accelerating the delivery of world-class care.
Lab automation involves a set of technologies to automate manual, high-volume tasks in clinical or research labs. In a growing number of cases, these technologies involve lab robotics and artificial intelligence (AI), including machine learning, deep learning, and computer vision. Laboratory robotics and automation can be applied to a range of processes and equipment, from benchtop instruments to stand-alone systems to microscopes. Depending on how they’re used, lab automation systems may be single-function or combine many different functions.
Clinical Lab Automation
Automation in a clinical laboratory focuses mainly on ensuring accuracy while accelerating the time and efficiency of diagnostic testing. Clinical labs tend to run around the clock. It’s extremely important for technicians in these labs to manage the large number of tests coming in from one or more hospitals or clinics.
The latest solutions in clinical lab automation use computer vision to read barcodes, identify samples, and help robotic arms make accurate movements. Clinical labs are also exploring the use of machine learning in areas like digital pathology, which requires a high level of compute performance on edge servers.
Research and Pharmaceutical Development
Liquid-handling robots, genomics sequencers, high-content screening (HCS), and high-throughput screening (HTS) are among the lab automation systems helping scientists accelerate research and pharmaceutical development. Researchers can perform an incredibly large number of experiments, which can lead to the discovery of new drugs, cancer therapeutics, and other treatments. Machine learning and deep learning are particularly valuable in research labs, with algorithms that accelerate HCS and other imaging workloads.
For example, to support early drug discovery through HCS acceleration, Intel and Novartis used deep neural networks (DNN) to cut the time to train image analysis models from 11 hours to 31 minutes1. The team used eight CPU-based servers, a high-speed fabric interconnect, and optimized TensorFlow to process microscopic images significantly faster. This solution helps researchers study the effects of thousands of chemical treatments on different cell cultures and evaluate the potential effectiveness of various drugs.
Benefits of Lab Automation
Automating manual processes in the lab leads to a number of benefits, most notably time savings. But even more important is what’s at stake when tasks are completed faster while maintaining accuracy. For example, when researchers can rapidly run a million compounds against a drug target, they can discover a breakthrough treatment at a speed never before possible.
- Error reduction. By design, lab automation reduces the possibility of human error by taking manual work out of the process2. This also supports reproducibility and consistency in testing.
- Fast turnaround time. Automated systems can perform high-throughput screening and other experiments at a pace not possible when performed by humans, all while maintaining accuracy.3
- Strategic use of human staff. Lab technicians and scientists can work at the higher end of their skill sets and focus their attention on strategic tasks, rather than being tied up with repetitive work.
- Cost reduction. Lab automation systems may help lower costs by reducing reagent volumes needed and minimizing waste.
- Workplace safety. By minimizing the need for human intervention, lab automation can help technicians limit exposure to pathogens and harmful chemicals or injuries caused by repetitive motions.
Intel and Novartis used deep neural networks (DNN) to cut the time to train image analysis models from 11 hours to 31 minutes1.
Lab Automation Technologies
From robotic arms to image processing, Intel® technologies power the latest lab automation solutions. Our broad portfolio of compute technologies gives instrument manufacturers a range of computing options that meet power and performance requirements, along with software-enabled capabilities for vision and other types of AI.
Additionally, servers and storage built on Intel® technologies provide a strong foundation for data management throughout the lab. This supports the principles of FAIR data—making data findable, accessible, interoperable, and reusable to automated systems without human intervention.
|Intel® Technologies for Lab Automation|
|Intel® Core™ processors and Intel Atom® processors||Intel processors deliver the right level of performance and power consumption needed to automate processes in the lab. Ideal for sample handling and retrieval, sorting, centrifugation, and other pre- and postanalytical functions.|
|Intel® Xeon® Scalable processors||Intel® Xeon® Scalable processors deliver high performance for edge servers in the lab, especially useful for high-content screening (HCS) and other types of imaging.|
|Intel® Movidius™ VPUs||Intel® Movidius™ VPUs are designed for computer vision at the edge. These low-power VPUs enable barcode reading, robotic arm movement, sample analysis, and much more.|
|Intel® Optane™ persistent memory and SSDs||Intel® Optane™ persistent memory and solid state drives (SSDs) support large in-memory applications, ideal for imaging and AI workloads in lab automation.|
|AI Software Tools4||For developers, Intel offers software libraries and optimizations for popular frameworks like TensorFlow and Caffe to boost performance on Intel® architecture. The Intel® Distribution of OpenVINO™ toolkit streamlines the development of vision applications on Intel platforms, including VPUs and CPUs.|
|Intel® Wi-Fi 6 and Intel 5G||With support for the latest Wi-Fi and 5G standards, Intel is streamlining the process of connecting instruments in the lab. High-speed connectivity enables remote control, real-time monitoring, and other edge-to-cloud use cases.|
Enabling the Lab of the Future
The Internet of Things has already begun to break down data silos and enable a new level of automation. Microscopic images are processed in real time. Experiment results can be analyzed and shared with labs around the world. Sensor data can be applied to AI algorithms to inform predictive maintenance, which in turn prevents instrument downtime.
Faster processing, storage, and network technologies will continue to enhance the efficiency of the lab of the future. For example, researchers at the Translational Genomics Research Institute (TGen) are sequencing patient genomes, then performing genomics analytics on a high performance computing (HPC) infrastructure powered by Intel® Xeon® Scalable processors. Using modern HPC hardware to perform analytics faster enables genetics counselors and physicians to identify more-timely treatment options. Modern HPC hardware also provides a foundation that empowers researchers to apply machine learning methods to massive amounts of data, revealing insights that can take precision medicine to new heights.
TGen has built a high-performance computing (HPC) infrastructure. Optimized for life sciences, it includes Intel® Xeon® Scalable processors, Intel® Optane™ memory, and Dell rack servers.
As clinical, research, and pharmaceutical laboratories become more connected and automated, Intel will provide a foundation of technology that moves, stores, and processes data efficiently. Whether it’s genomics analytics in the cloud or robotic arms at the edge, Intel® technologies enable intelligence at every step in the automated lab.
Frequently Asked Questions
Lab automation uses robotics, AI, and other technologies to automate manual, high-volume tasks in clinical or research labs.
Automation can accelerate turnaround time and discoveries in both clinical and research labs. This includes labs in hospitals, pharmaceutical and biotech companies, universities, and other research institutions.
Laboratory robotics and automation are powered by a range of hardware and software, sometimes with special capabilities for computer vision or other types of AI.