Life sciences sector is grasping the power of AI
The pandemic has got all of us thinking differently. As crazy as it may seem, at Accenture’s digital lab in Leopardstown, near Dublin, a team of 80 people have started to brew beer to learn more about the manufacturing process of pharmaceuticals.
By replicating the fermentation process in beer production and using a combination of artificial intelligence and data analytics to study it, they can begin to see how making drugs – and critically vaccines – can be made more efficient. In many ways it makes perfect sense. Using data, you can find out during the process if your live batch of beer is being made to specification. The same principles can be applied to live pharmaceuticals. And the conse - quences of getting this right couldn’t be more far-reaching. Why make a batch of vaccines only to discover at the end of the process that it isn’t as it should be?
The intriguing thing is that this can be achieved if you maximise the use of scientific data and apply AI and data analytics.
Covid-19 has accelerated the use of new technologies in many areas of business and life science is no different. Look at how the World Health Organisation, Oracle, Microsoft, IBM and others are collaborating on HACERA’S Mipasa, a blockchain-based open data hub that aims to quickly identify Covid-19 carriers and hotspots. Or look at Los Angeles’ Cedars-sinai hospital, which is using VR simulations to train doctors to treat infectious diseases. Healthcare that is weaving technological building blocks together is setting a course for the future.
AI, in particular, offers a unique advantage. Life science start-ups are disrupting decades-old incumbents because the AI technology they embrace doesn’t approach a problem based on years of experience or inherent human biases. It hasn’t yet learned what not to try. This blank slate offers fertile ground for transformation in healthcare. Overwhelmingly, AI can become an agent of change, transforming not just how organisations do work – through automation and execution – but also what they actually do.
In this new era where physical distance has become a requirement, not a preference, AI can help treat people at home. Smartphones equipped with sensors can continuously monitor a variety of health issues, including respiratory conditions. Algorithms identify and classify the severity of coughs or flag breathing irregularities so that care providers can intervene when issues crop up, no matter where the person is when they arise. AI is increasingly supporting the development of treatments and advances the manufacturing processes. It is not surprising, therefore, that research shows that 69 per cent of healthcare organisations worldwide are piloting or adopting AI. In Scotland, investment in life sciences over recent years has been robust. Scotland is now home to one of Europe’s largest life sciences clusters, employing nearly 40,000 people. In 2019, a survey found that more than half of the nation’s life science organisations were reporting increased turnover and profits, with nearly two thirds signalling an onward upward trajectory. If that growth potential is linked to the manufacturing and technology expertise deep-rooted in this country and aligned with data science and AI (both devolved matters in Scotland) there is even greater opportunity. As we continue to develop a national AI strategy for Scotland, a dedicated focus on life sciences would surely reap rewards.
As healthcare organisations adapt to new ways of working coming out of the crisis they are realising that maturing digital technolo - gies, scientific advancements, and emerging new tech platforms will help bring new ideas to fruition and better still, save lives. If, in Scotland, we manage to seize opportunities and maintain the momentum and pace of development, it is hard not to feel excited by life sciences. The building blocks to grow the sector further are moving firmly into place. Michelle Hawkins, joint MD of Accenture Scotland
In Scotland, investment in life sciences over recent years has
been robust