Never before has the global community been so fixated and reliant on the creation of a vaccine. Without it, life is unable to return to normal and we will have to adjust to a new norm that could last years. During this pandemic, we continue to rely on our doctors, care-workers and nurses, along with our scientists to steer us through this crisis. However, with many laboratories lagging behind in terms of technological and digital progression, productivity levels in science are not what they could be, a factor which undoubtedly slows advances in science. To prepare for future pandemics and other challenges we will inevitably face, we must implement technologies that will improve our efficiency in the laboratory, research centres and our hospitals. In doing so, we will not only offset the burden on our health workers and scientists, but we will also maximise the opportunities disruptive technologies create.
For the fourth consecutive year, the US government called for major cuts to science and health agencies with the exemption of certain favoured research, deemed essential to national security . In this critical time, investing in research and development should be of top concern, to ensure that we are fuelling the scientific response to this pandemic. Cutting funds to research and development at such a critical time is a decision that could and has cost countless lives.
So why is it important to invest in research and development?
Increasing funding for research and development would enable scientists to invest in digital solutions which, as a study conducted by Mckinsey suggests, can lead to 30 to 40 % increases in productivity within already mature and efficient lab environments , and 50 % in overall quality-control costs . What makes this environment suited to digital technologies is its nature: it is comprised of a complex array of isolated machines and devices, designed to ensure standardisation, accuracy and replicability of experimental data. Now, imagine the benefits that can be had from connecting all laboratory components: the devices, data and the researchers. The introduction of technologies such as automation has shown to significantly reduce human error, that as many in science can agree on, has plagued even the most esteemed researchers.
The proposal is not to suddenly splash the cash’ and invest thousands on an entirely new system but instead to build on the existing features of the laboratory to make the environment smarter. Software solutions such as the digital laboratory notebook, the laboratory execution system and the laboratory information management system can be implemented with no significant cost, yet have been proven to substantially increase replicability, accessibility and the compliance of scientific experimental data. Improving these areas can make the laboratory far more productive, and as a result, experimental research produced can be generated at a faster rate whilst still ensuring the same, if not greater levels of accuracy.
The purpose of laboratory digitalisation is to maximise both researcher and machine potential. But even with the successful implementation of forms of IoT and laboratory automation, it can and should be taken further. For that, Artificial intelligence (AI) and machine learning are required.
Recently we have witnessed the maturing of many breakthrough technologies, though none are perhaps as revolutionary and controversial as AI. Some say that this crisis has exacerbated the need for Artificial Intelligence, while others question whether the introduction will be dangerous, not only in terms of security but also in the threat to people’s jobs and livelihoods.
The economic advantages of AI, however, are simply too great to ignore, especially in these unprecedented times, which have led to many economists forecasting severe negative impacts . An IBM article accentuated that AI has the potential to offer $15.7 trillion to the global economy by 2030 . It posits that there will be changes in the way we work, but these changes will offer opportunities rather than threats.
Regardless of the overall impact of AI, in the laboratory environment and in healthcare, it’s transformative power has been hailed as the key to making drug development, research and hospital care not only more efficient, but also more effective, both in practice and monetarily. AI in the laboratory is able to tie in all of the technologies above, it offers a further layer to the laboratory system by analysing all experimental data collected by experiment devices, whether it be a sensor or a collaborative robot.
From data collected, AI is able to produce hypotheses and predict which combination of materials or temperature is desired for the experiment. In short, this system will allow scientists to be aided by a highly intelligent system which is constantly monitoring and analysing the experimental output. In this way, AI will help an experiment from its inception to conclusion.
