How can AI advance laboratory research?

Despite often standing at the cutting edge of technology, lab operations often involve many repetitive, manual tasks. These inefficiencies can slow the discovery of disruptive and even life-saving innovations, but AI is strongest in these areas where humans are weakest. As this technology improves and more labs implement it, AI could accelerate and refine laboratory research.

Running complex simulations in minimal time

One of the biggest advantages of AI in laboratories is its ability to run massive calculations in mere seconds. This ability lets machine learning models simulate experiments or analyse results far faster than a human could. This speed can significantly reduce the research and experimentation timeline, leading to quicker and more accurate results.

This efficiency played a vital role in the rapid development of some COVID-19 vaccines. When Moderna turned to AI to produce mRNA strands for testing, it could generate roughly a thousand strands a month when manual methods could only make 30. As a result, Moderna’s lab researchers were able to test more candidates in less time to find the ideal one for the vaccine.

Similar technologies could simulate drug-disease interactions to find promising medicine candidates before moving to real-world trials. Alternatively, labs could use AI to find ideal testing groups for new solutions. Regardless of the context, these technologies accelerate the pace of innovation.

Reducing workload

Running complex calculations in seconds isn’t the only way AI can streamline lab operations, either. This technology can also automate repetitive, less value-adding tasks to reduce researchers’ workloads, giving them more time to focus on more sensitive issues.

Thorough scientific research requires extensive record-keeping, but manual approaches to this take time. With AI, labs can automate data entry, reporting and similar administrative tasks to let researchers focus on the actual lab work. As a result, they can devote more time to more impactful work in a day, helping them stay productive and further shorten project timelines.

As this technology improves and more labs implement it, AI could accelerate and refine laboratory research

Lighter workloads will also make it easier for researchers to focus on more important issues. This increased focus will help avoid mistakes and aid discovery by reducing stress and preoccupation.

Minimising human error

That last benefit of reduced workloads leads to another important advantage of AI in laboratories. Automating repetitive, data-heavy tasks through AI helps reduce the likelihood of human error in the research process.

Just as automated physical lab equipment minimises errors luch as slips and vibrations, automated software minimises data mistakes. AI will deliver the same level of precision and thoroughness with every dataset and is often better at drawing connections between data points than humans are. Consequently, it produces more accurate, reliable results.

Reducing human errors does more than help improve test accuracy and facilitate discovery, too. It also minimises the timely, potentially costly rework process that’s necessary after making a mistake. This further accelerates the research timeline and can make it more cost-efficient.

Synthesising test data

Another way that AI can advance laboratory research is by providing more data for researchers to analyse. Advanced machine learning models can synthesise datasets based on real-world examples. These synthetic datasets follow the same format and offer the same value as random real-world information but make it more accessible and secure.

Because synthesised data follows trends and formats that real-world examples exhibit, it behaves the same. If labs need anonymous, random data for their research, these datasets will provide the same reliability, too. They’re also better for privacy, as any data leaks won’t expose real people’s personal information.

Because synthesised data follows trends and formats that real-world examples exhibit, it behaves the same. If labs need anonymous, random data for their research, these datasets will provide the same reliability, too

This advantage can even address one of the biggest challenges people face with AI itself. Intelligent algorithms require massive datasets to work reliably, but that much real-world information is difficult to acquire. Synthesising data to train new AI models offers a cost-effective and fast solution to that obstacle.

Enabling continuous improvements

Using AI in laboratories will also help research and scientific advancement accelerate exponentially over time. While many simple algorithms are strictly rule-based, more complex machine learning models factor in patterns and outliers, too. As a result, they can gradually learn and improve themselves, becoming increasingly accurate the more they work.

These improvements in AI models can help lab workers review and assess their broader research approaches. Over time, this pattern of reviewing reveals what works and what doesn’t, pushing scientific research further.

These changes may be small at first, such as reworking a process to save an hour or improving process result reliability by a few percentage points. However, these incremental improvements will add up to considerable advances over time.

Accelerating research

AI is still a relatively new technology, and it’s far from perfect in its current form. However, its potential is already substantial. As more labs implement these technologies, the scientific field will become more efficient, accurate and resource-friendly. Researchers have yet to understand AI’s full potential, too. As the technology improves and sees more use, it could revolutionise scientific research. 

PIC: thisisengineering

Author: Emily Newton is the Editor-In-Chief of Revolutionized, a magazine exploring innovations in science and industry that shares ideas to promote a better tomorrow.