John Pugh, healthcare and life sciences lead at Slalom, outlines the importance – and looks at some specific aspects of – strategy when deploying AI in the sector.
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Slalom
John Pugh, healthcare and life sciences lead, Slalom.
Generative AI (GenAI) is set to revolutionise many sectors, but the most impactful and human-centric industry it is revolutionising is healthcare and life sciences.
The inherent intricacies of biological systems, increasingly specialised patient cohorts and a competitive landscape, necessitate evolution beyond traditional discovery and development paradigms within the life sciences industry. Gen AI will be key to this. It will no doubt reshape the research and development (R&D) landscape and the impact on patient outcomes could be huge.
It’s clear the UK Government recognises the opportunity AI brings to life sciences too with chancellor, Jeremy Hunt, recently announcing £100 million in funding specifically to “capitalise on AI’s game-changing potential in life sciences and healthcare.” It's not just the Government that sees the opportunity that AI represents, though. According to some recent research undertaken by Slalom, 93% of businesses in the life sciences space are already using AI, with a third of those saying AI is used throughout their organisation. While it’s positive to see this passion and early uptake of AI across the sector, we must recognise the myriad of challenges that must be overcome for AI to have the impact we’re all hoping for regarding patient outcomes.
Further to the challenges on hand, there is a significant opportunity for businesses, especially those in the life sciences industry, and that’s to grab the AI opportunity with both hands, but at present, just 3% of businesses in life sciences claim they have a joint up AI strategy in place – which leads to the question; without a robust AI strategy, will the life sciences sector achieve its AI ambitions?
Why AI presents such an opportunity
In a world of organ-on-a-chip technology, machine-generated data and decentralised clinical trials, a business with a cohesive and scalable R&D strategy that integrates GenAI will create opportunities and drive towards delivering promising outcomes for patients. From early drug discovery and development, to personalised medicine, redefining medical devices and speeding up diagnoses, the potential of AI is vast in life sciences.
If we examine the rise of precision medicine and acknowledge the fact that most successful drugs are only beneficial for 30% of patients, GenAI can review and analyse vast amounts of data, including biomarkers, in seconds, to suggest which existing drugs are best suited to specific patients. Ultimately this generates better patient outcomes both in terms of treatment, but also patient experience because the patient will no longer need to trial drug after drug to find the best fit. Instead, they can walk away with the medication best suited to them after the first appointment and the knock-on impact of waiting times for other patients reduce as a result.
Beyond that specific example, adoption of GenAI in life sciences allows more objective and unbiased data interpretation. By training and deploying GenAI systems that work towards unbiased measurements, we can create a more reliable, replicable and valid outcomes, that in time can be shared across the sector.
Ultimately, when used effectively and with strategy in mind, AI, and GenAI specifically, will not only help researchers in the life sciences sector, but also clinicians with clinical care delivery decisions to create a more personalised experience for patients, that leads to better outcomes.
Why strategy matters
With life sciences businesses keen to adopt AI, 93% already using it and the vast array of positive outcomes it could produce, it would be fair for patients to ask why they’re not seeing the benefits already in their day-to-day. If we have the components and the opportunity, what’s holding us back? This is where strategy comes into play.
While AI has its benefits and may fit seamlessly into one area of a business, it might be more challenging to see where it can support in another. Life sciences businesses taking a siloed approach, whereby they implement AI in different distinct areas of their organisation, are missing the opportunity that it presents. Instead, the emphasis should be on putting AI at the heart of your business strategy and working to understand how it can support every area of your work. Not only is this more productive, it’s also crucial to recognising and overcoming the very real challenges that AI represents in the life science industry. Some businesses have found that whilst conducting masses of experiments they now face the challenges of finding the resources and skillset to productionise and scale (41% cite a lack of internal expertise as their biggest barrier to success).
Take one of the key challenges facing the sector - the shortage of professionals who are skilled in both AI/machine learning and have biological expertise. A siloed approach might be to rely on one individual or one department to create and understand the AI output, putting a lot of strain on one specific team or team member. A more strategic and joined up approach that puts AI at the heart, would be to acknowledge the challenge that expert scarcity represents and consider how you can make the most of this person’s skillset, using AI to upskill others across the business so colleagues learn together, mitigating risks of knowledge scarcity. Acknowledging this is a significant change across the workforce is key too, and bringing in change management teams to work alongside your soon-to-be upskilled workforce will allow them all to be part of the AI transition, again, removing the pressure on the already-skilled individual or team.
Prioritising interdisciplinary education is paramount in AI strategy, as without meaningful expertise at scale, we’ll not meet the full potential of these models to change life sciences research. A holistic AI strategy is key to acknowledging this challenge and helping overcome it.
Final thoughts
AI’s potential in the life sciences sector cannot be understated, but neither can the importance of strategy to implement it. Improving patient outcomes, whether that’s speeding up diagnoses or improving patient experience using big data that tracks patient journeys outside a healthcare setting depends on a collaborative and cohesive approach to AI.
For us as a sector to truly realise – and capitalise on – the opportunity that AI presents, businesses must not view AI as a means to an end, but as an integral part of their day-to-day, not only when it comes to clinical data, or creating new innovative medical devices – but when it comes to the success of their business.
When we look back in years to come, the businesses that we think of as revolutionary in life sciences and med tech will be those who implemented AI in every facet of their business at this early stage.
