Science must be accurate and precise. Throughout the past decade, lab informatics tools, robotics and artificial intelligence (AI) have all helped research labs to improve experimental accuracy, precision, output and speed (1). This trend will continue into the next decade, however the focus will shift to making sure informatics tools are user-friendly and interactive.
As we move into 2020 and beyond, digital tools will become more user-focused and interconnected. Lab informatics and instruments will communicate with each other to form the integrated, smart Lab of the Future. Once we start seeing digital tools with simple, user-friendly interfaces, adoption should increase, leading to lower error rates, improved experimental efficiency and better use of data. With wider adoption of next-generation digital tools and advances such as faster 5G/6G internet connections, we should continue to see the cost of automation and data generation decrease.
User-Centered Design
Scientists spend 50 to 80% of their time at the bench, yet, until recently, there was no reliable way to capture and access data while running experiments. Many researchers still have to stop experimental work to record their findings or access information. This ‘data desert’ interrupts workflows, increases error rates and makes it difficult to collect data.
This problem has been solved by creating digital tools that truly serve the needs of scientists. A new generation of digital tools leverage mobile and voice technology so scientists can access and record data, control lab instruments, set timers and reminders and order reagents from anywhere in the lab.
Throughout the next decade, we will see more life science informatics companies focus on user-centered design. Digital lab assistants will use intuitive interfaces such as voice interaction, visual and audio cues so scientists can control experiments from their office, home, or even while traveling. Hands-free, mobile technologies will replace stationary equipment and software. These advances will mean that scientists can access data in real-time and therefore, make real-time, data-driven decisions before, during and after an experiment.
New lab informatics tools can also collect metadata such as data about experimental conditions. Simplified, interactive access to this sort of data will make it quick and easy to find the cause of any experimental errors and thus improve experimental reproducibility.
Connectivity and Interoperability
Interoperability in the lab is another major trend that will continue into 2020. Currently, R&D companies use many tools to support their research, but most of the time, these tools are not connected which means data is fragmented. Therefore, scientists must waste time collating and harmonizing data. Not only does manual data entry waste time, it is also a repetitive task prone to errors(2).
In 2020, new lab informatics tools will automatically collect and collate data within a single platform, allowing scientists to access all their lab data in one place. In the next decade, we will hopefully see all smart digital tools and lab equipment communicate with each other so experiments run seamlessly.
Life science companies are already striving to make that vision a reality. Rather than purchasing machines that only perform one type of experiment, companies are starting to look for modular tools to create a multi-purpose ecosystem. This approach will allow scientists to run many different types of experiments within a single setup. To achieve such an integrated, adaptive system, companies need to know how any new tool could communicate with existing software and equipment, and its capacity to integrate with future instrumentation.
This creates real challenges for lab informatics companies. We now need to design tools with the capacity to seamlessly interact with any number of instruments and databases, as well as legacy and next-generation informatics systems.
Cost of Automation
As the life science industry continuously moves towards the Lab of the Future, research spaces are becoming more connected, touchless environments. In the near future, scientists will be able to control instruments and record data without using their hands. These advances should help increase productivity and shorten product development timelines. Research shows that simplifying, automating and documenting experimental processes can lower error rates and improve outputs(1,3).
Automation has been slowly creeping into research labs over the past 20 years. As with all new technologies, the cost of automation has dropped over this time. In 2020, the cost to automate R&D will continue to decrease with the availability of 5G and even 6G networks, advancements in the Internet of Things, and new smart instruments arriving every week. Most of these smart tools will be able to talk to the internet and to each other. Faster internet access and more cloud-based informatics tools will simplify secure data exchange and allow scientists to remotely control experiments.
The Lab of the Future
The Lab of the Future is fast approaching. More research labs are already adopting cloud, mobile and voice technologies. Over the next year, the life science industry will move closer to its next stage of evolution where lab operations will be touchless, connected and automated. Smart lab tools will work in the background, seamlessly integrating with each other to support scientists, reduce manual labor and improve research efficiency. Rather than replacing humans, lab informatics and AI tools will augment the work of human scientists. The best results come when AI and humans work together (4-6). However, first we must create tools that humans want to use.
Guru Singh is the Head of Growth at LabTwin.
References
1. Genzen, J. (moderator) Challenges and opportunities in implementing total laboratory automation. Clin Chem 2017
2. Chevez Bernaldo de Quiros, A. Human reliability as a source of error in research in Dainty, Andrew (ed), 24th Annual Conference of ARCOM. 2008
3. Plebani, M. The detection and prevention of errors in laboratory medicine. Ann Clin Biochem 2010
4. Valeriani, D. Humans and machines can improve accuracy when they work together. Phys Org (Online) Accessed 27 November 2019 at https://phys.org/news/2019-03-humans-machines-accuracy.html
5. Livne, T. Why AI and humans are stronger together than apart. Entrepreneur. (Online) Accessed 28 November 2019 at https://www.entrepreneur.com/article/329099
6. Wilson, H. and Daugherty, P. Collaborative intelligence: Humans and AI are joining forces. Harvard Business Review (Online) Accessed 28 November 2019 at https://hbr.org/2018/07/collaborative-intelligence-humans-and-ai-are-joining-forces
Guru Singh, Head of Growth at LabTwin