Artificial intelligence (AI) and big data are currently revolutionising the life sciences industry. According to recent analysis from Deloitte, the potential market impact of AI applications in global life sciences is projected to reach US$5–7bn by 2025. And while the United States currently dominates this emerging market, opportunities exist for other regions to make significant contributions. We spoke to Michael Dillhyon, an accomplished US-based (formerly Swiss-based) founder, mentor and adviser about the transformative potential of AI and big data in life sciences – and how Switzerland can position itself at the forefront of this revolution.
The rise of big data and AI: Breaking down silos to further innovation
‘We’re at a real inflection point, as happens with just about every wave of technology,’ Michael said with a laugh. ‘Things develop slowly and theoretically for a while, and then suddenly everything happens all at once – a bit like my attempts to learn Swiss German.’
Indeed, the landscape has evolved dramatically over the past decade. During his tenure as Chairman of the Board at GeneBio between 2009 and 2013, Michael experienced firsthand the limitations of siloed data systems. ‘Back then, we simply didn’t have the computing power to effectively combine and analyse different data sets,’ he reflected. However, he went on to build on this experience in his later work with Elemental Machines, a company that pioneered the integration of Internet of Things (IoT) technology in pharmaceutical R&D, demonstrating how connected devices and real-time data analytics could transform laboratory operations. Michael explained: ‘Today, we have sophisticated AI learning models that can compare and synthesise rich data sets for the first time, opening up entirely new possibilities. The leap from siloed databases to interconnected IoT devices and AI analysis has been remarkable to witness and be part of.’
This technological advancement has particularly impacted R&D, which Deloitte’s analysis suggests could account for 30–40% of AI’s potential market value in life sciences. AI is totally transforming the drug discovery and development process, making it significantly more efficient. Michael contrasted this with traditional approaches: ‘Previously, companies would sometimes employ a “spray and pray” trial design – going shallow and wide in the hope of finding something interesting. This approach wasted considerable time and resources.’
In fact, the development of robust data infrastructure and standardised protocols has created what Michael described as a positive feedback loop: ‘Current market innovation cycles have compressed to between 18 and 24 months for certain applications. We’re projecting that development costs could decrease by up to 50–60%, which means more resources available for new ideas and increased chances of return on investment. After all, everything in this ecosystem is interconnected.’
And the impact extends far beyond research laboratories. ‘AI-powered tools are now being used everywhere,’ Michael noted. ‘This widespread adoption means we’re gathering unprecedented amounts of real-world evidence across entire patient populations, creating opportunities we’re only beginning to understand.’