Just about every industry you can think of has, in one way or another, been exposed to the prevalence of opportunities available through the Internet of Things (IoT). By the year 2020, the number of “things” connected is projected to be about 20.4 billion. By 2025, IoT is projected to generate $3.9 to $11.1 trillion every year to the economic value.
IoT is expected to contribute to the healthcare and pharmaceuticals sectors about $154 billion by 2025. No wonder every CEO is feeling compelled to participate in the revolutionary Internet of Lab Things (IoLT)! Below are some of the IT trends you should expect to transform lab work in addition to automated tasks such as time tracking, find more details here.
Microfluidics and Lab-on-a-Chip Technology
Microfluidics allows the manipulation and analysis of minute volumes of fluid in the multichannel system. Even more attractive is the ability to downsize large-scale biology and hosting multiple experiments on one chip that’s small enough to fit in your palm.
Miniaturizing multiple laboratory operations is already occurring in organic chemistry, molecular biology, genomics and materials science. This technology allows labs to require little samples while reducing the volume of reagents required, translating to an overall cost saving for any lab. Another benefit is that a miniaturized system offers high-resolution analysis, but still maintains sensitivity.
The miniaturized samples mean that reagents will rapidly diffuse in the reaction chamber, reducing the time it takes for a reaction and generating results faster. This means that researchers spend less time waiting for results and more time working on research, resulting in better return on investment. Lab-on-chip systems will also automate and standardize procedures with little human intervention and fewer chances of human error occurring.
Robotics and Automation
It’s an understatement whenever anyone says that technology has changed the way research is conducted – it has transformed research! Robotics will see the invention of liquid handling robots in a wet lab that are capable of handling thousands of samples with limited supervision. This automation will allow researchers to spend more time on data analysis through tools like software for colony management.
Machine Learning, Artificial Intelligence and Cloud Computing
Researchers benefit from easy and secure access to critical data from multiple research groups through cloud computing. Through this technology, the researchers can also access real time data that improve collaborative capabilities across the world.
Advances in technology are fundamentally influencing how research in the lab is conducted. As cloud computing makes collaboration easier, there is a growing need for efficient data handling systems. This in turn has spurred the development of machine learning and AI solutions.
Already, labs all over the world are making significant investments and achieving stronger collaborative effort through AI research. However, as remarkable as the systems are today, AI still requires innovations that will help tackle real-world problems to improve work and lives in the lab.
Machine learning allows the researcher to thoroughly interrogate structured and unstructured data via self-learned algorithms. The key to achieve true machine learning in the lab is through accessing as much data as possible. Incorporation of machine learning and text analytics in research workflows makes it easier to scrutinize data, drive hypotheses forward as well as establish the direction future research takes.
What to Expect
Already, the way experiments are conducted in the modern lab has been transformed by microfluidic technologies by reducing cost and scale. Furthermore, machine learning, AI and cloud computing are making it easier for researchers to access, share and analyze data from their experiments. As the laboratory evolves with the help of IoLT systems, expect huge technological advancements that will bring you closer to the fully automated intelligent lab of the future.