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05.12
2024

How Switzerland can become a global leader in the transformative field of AI in the life sciences

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.’

Today we have sophisticated AI learning models opening up entirely new possibilities.

Managing challenges in this fast-moving landscape

Despite the promising advances, significant obstacles remain. For one thing, according to recent research by Hyoun-Joong Kong, about 80% of medical data remains unstructured and untapped after it is created in formats such as text, images and signals. As Michael went on to explain: ‘It’s not just about having the data, it’s about making it usable. We’re sitting on a goldmine of medical information, but right now it’s like having a library where all the books are written in different languages, using different alphabets and some are even in code. The real breakthrough will come when we cannot only read all these “books” but understand how they connect to each other. In short, it’s a mammoth undertaking.’

The integration of AI with legacy systems represents another major challenge – one that Michael has experienced firsthand. ‘I’ve seen venture investment committees get more excited about ChatGPT writing their investment memos than about revolutionary drug discovery platforms,’ he said with a mix of amusement and frustration. ‘We’re literally using AI to potentially cure diseases, and their most pressing question is whether it can generate better pitch decks.’ In this, Michael drew a parallel with previous technological transitions: ‘If we look at cloud computing as an example, it took nearly a decade for widespread understanding and effective implementation. We’re on a similar journey with AI in healthcare.’

Michael also shared his perspective on the controversial topic of AI’s impact on employment and data privacy. In Geneva, he worked with an exceptional team of healthcare innovators, data privacy experts and patient advocates to launch healthbank AG, the world’s first patient-owned health data platform. ‘Let’s be honest here – people are worried about AI stealing their jobs, but at the same time, they voluntarily hand over their entire life story to social media companies,’ he stated bluntly. ‘The same person who’s concerned about AI accessing their anonymised health data probably just took a quiz on Facebook that asked for their mother’s maiden name and first pet’s name.’

Drawing from his healthbank experience in Switzerland’s data-conscious environment, he continued: ‘The real conversation we should be having isn’t just about protecting data – it’s about who owns and controls it. When I founded healthbank, the idea of patients owning their health data was radical. Now it’s becoming increasingly clear that this model isn’t just about privacy – it’s about empowering people to make their data work for them and for the greater good of medical research.’ Taking a more serious tone, he argued: ‘AI is not going to replace a single human; it’s just going to enhance their ability to make better decisions.’

Regarding data protection, he emphasised the importance of working with existing regulatory frameworks: ‘We have robust protections in place through GDPR in Europe and HIPAA in the US. We just need to work within these frameworks to manage risk and build patient trust, while also continuing to advance healthcare innovation.’

We’re gathering unprecedented amounts of real-world evidence creating opportunities we’re only beginning to understand.

Switzerland’s potential in the global AI landscape

While US companies currently dominate the global AI market, with Transforma Insights reporting that two-thirds of AI firm acquisitions between 2021 and 2023 involved US-based companies, Switzerland possesses unique advantages that could position it as a major player in this field.

‘Switzerland has the potential to become every bit the powerhouse that the US and other leading regions are,’ Michael asserted. Indeed, he pointed to several distinctive Swiss advantages: ‘The country combines rich technological architecture spanning multiple industries, a tradition of precision manufacturing that aligns perfectly with AI’s requirements and world-class research institutions – all within a remarkably concentrated geographic area.’

To capitalise on these advantages, Michael recommended an integrated approach: ‘The key is to create comprehensive ecosystems that accelerate the transformation of academic innovation into commercial success, while developing systematic approaches to scaling these innovations.’ He also emphasised that success will come not from individual achievements but from building on Switzerland’s traditional strengths to create ‘unique models and solutions that aren’t easily replicable elsewhere’.

The future of AI in healthcare: A vision for 2040

Looking ahead, Michael envisioned a transformed healthcare landscape centred around preventative medicine and enhanced by AI technologies: ‘Imagine a healthcare system where clinicians have access not just to your current symptoms, but to a comprehensive history of health data, enabling more informed decisions and early intervention.’
Drawing an analogy with preventative maintenance in other fields, Michael made the following suggestion: ‘We could move towards a model where, like modern vehicles with sophisticated monitoring systems, our bodies’ health statuses are continuously tracked, and potential issues are identified before they become serious. Within ten years, we could see up to 80% of medicine focused on preventative care.’

This transformation could also help address healthcare inequalities. ‘AI-enabled, decentralised care could bring advanced healthcare services to underserved populations,’ Michael noted. ‘While we must remain vigilant about algorithmic biases and maintain transparency, the ultimate goal is to ensure everyone has access to quality healthcare that supports a good quality of life.’

The vision extends to a universal framework for health data sharing, which Michael believes could revolutionise healthcare delivery. ‘With the right investment in technology and infrastructure, we could create a system that combines AI-enabled monitoring with human medical expertise, making sophisticated healthcare more accessible and efficient for everyone.’

Michael Dillhyon

Michael Dillhyon is a serial entrepreneur, investor and digital health pioneer who has shaped the intersection of technology and life sciences globally. Currently serving as General Partner at Sarasota Growth Ventures, he combines deep operational experience with strategic investment expertise. A US-based business leader with strong ties to Switzerland, he has founded and advised multiple successful technology ventures. Michael’s expertise in start-up ecosystem development, data-driven healthcare innovation and patient-friendly medtech has made him a sought-after speaker in over 30 countries, particularly on topics of strategic value creation and innovation.

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