The Future of Lab Informatics

Laboratories continue to benefit from innovations that will speed discoveries, increase productivity, and streamline operations across biology, agriculture, medicine, nanotechnologies, and hundreds of other industries. As funding is going to still be a challenge, companies will need to increase their focus on innovation and adoption of new technologies to remain competitive.

We foresee that these will be the most active areas of technological growth and laboratory transformation in 2025 for R&D and QC labs.

1) Voice

Voice-activated instrumentation and recording systems will become increasingly popular as the advancements in generative AI will make these tools better and easier to use.

Using voice-activated instruments and electronic lab notebook (ELN) software will speed up documentation and make it much easier to follow protocols while working in sterile environments. This will also allow differently abled scientists fuller participation in their research.

Furthermore, labs will reduce long-term capital and operational costs by eliminating the need for a physical computer on the bench.

2) Augmented reality

Augmented reality will help scientists work remotely to tackle complex problems together, providing guidance on instrument troubleshooting. Integrated AR will also reduce the need to open freezers to check if items are there.

3) Expanded use of AI and large language models (LLMs)

LLMs will go beyond novelty and general interest topics into more specialized fields, especially as they relate to lab informatics. LLMs that better understand scientific work will speed the formulation of hypotheses and provide input into the scientific “rationality” of theories. Using LLM for real-time data quality control will reduce QA time.

AI can also be used to improve safety and ensure safety protocols are met by tracking each process and analyzing log files automatically, providing both operational insights and streamlining regulatory reporting.

4) Smarter instruments

Instruments will be increasingly orchestrated directly into laboratory information management systems (LIMS) and ELNs, making it easier to document experimental workflows and ensure data integrity.

The direct integration allows the LIMS and ELNs to track usage; that, combined with AI analyses, will make it easier to implement predictive maintenance programs, increasing the lifespan of the instruments.

5) Design of experiments (DoE) and predictive analytics

 AI models will be increasingly used to predict experimental outcomes, improving decision-making. Furthermore, design of experiment (DoE) systems will be more intelligent and will be able to predict the outcomes of more complex experiments. Validation is critical, of course. Over time, the results will become more accurate and trustworthy.

6) Increasing automation

DoE will not reduce the number of tests being executed but will improve the results. In addition, labs will invest more in automated mobile robots to better control sample management and perform the experiments without human supervision, increasing lab working hours and overall productivity without increasing operational costs.

 Labs will increase their usage of barcodes, speeding adoption of lab automation platforms and stores for storing plates and samples.

7) Growing focus on sustainability

Labs will need to demonstrate compliance with sustainability requirements, providing details about the activities they perform to reduce their carbon footprints. Dashboards will become more commonplace, making it easier to monitor performance and efficiency.

 This will increase pressure on labs to reduce freezer space and increase dependency on lyophilized samples.

Over the next year, we expect that as researchers integrate and adapt to these new technologies, results will be delivered more rapidly and accurately. Labs will streamline operations, increase efficiency, and drive discovery more rapidly.

Jonathan Gross is CTO and founder of Labguru responsible for a secure, cloud-based data management platform, including an Electronic Lab Notebook (ELN), LIMS, and an informatics platform, along with molecular biology and chemistry tools to facilitate lab automation.

Pic: Shutterstock (Zyn Chakrapong)