There are few industries more ‘human’ than life sciences.
It’s the industry that has improving the health and wellbeing of the population at its core.
Yet artificial intelligence (AI) is poised to transform the sector in profound ways. From accelerating drug discovery and development, to enabling precision medicine and enhancing clinical decision support, the opportunities presented by AI are enormous.
However, many life sciences companies are still grappling with where to begin on their AI adoption journey, and how human and machine intelligence can co-exist.
According to a skills gap report by the Association of the British Pharmaceutical Industry (ABPI), 70% of life sciences organisations ranked data science skills as a high priority need. At the same time, advanced data and digital capabilities represent the sector's most significant skills gap.
There’s never been a more important time for a human-first approach to AI.
AI’s transformative potential in life sciences
So what are the top use cases where AI can drive impact in life sciences? We've identified three key areas:
1. Accelerating drug discovery and development
AI is shaking up drug discovery. For pharmaceutical and research companies, AI can identify the most promising candidates for trials by rapidly screening huge libraries with millions of chemical compounds to find the right match.
AI can also assist with drug development through predicting drug-target interactions, optimising drug design, and automating repetitive tasks such as image analysis, data processing, and literature searches.
2. A ‘personal touch’ through precision medicine
Precision medicine is all about personalisation - tailoring healthcare strategies to each individual based on their unique makeup. By analysing a person’s data, including their genes, medical record, lifestyle and environment, AI can develop specific treatment plans that fit their circumstances like a glove.
It can even identify biomarkers that could catch diseases earlier and monitor how well a treatment is working.
Perhaps most exciting, is AI’s ability to make sense of the complexity of genomic and molecular data, like DNA and RNA sequencing. By mapping genetic variants and mutations to diseases and drug responses, AI could lead to new treatments or preventative strategies.
3. Supporting clinical decision-making
Clinicians have long been tasked with detecting tiny abnormalities in a sea of medical images. Now, AI can assist by automatically analysing X-rays, CT scans, MRIs, and even microscopic pathology slides to spot issues that might escape the human eye.
But AI can go much further with supporting human decision-making. By learning from the ever-expanding body of medical research, AI can act as a copilot for clinicians by recommending treatment options, identifying adverse drug effects, and even alerting clinicians about next steps.
Establishing trusted and successful AI
The potential benefits of AI in life sciences are clear; more targeted and effective treatments, accelerated drug development, and improved patient outcomes.
But as a highly-regulated sector, proper AI governance that builds confidence and trust is critical. This should be within a well-structured, risk-based approach to experimentation and adoption.
The latest introduction of the EU’s AI Act in August 2024, with its international scope applying to all companies that have supply chains within Europe, makes this even more imperative.
4 key lessons for adopting AI in life sciences
So how can life sciences leaders begin harnessing AI's power, while bringing the business and patients along with them? Here are some lessons we’ve learnt from supporting clients to accelerate their adoption journey while closing their own digital skills gap.
1. Begin by building a strong data foundation - focus on not just the technology platforms, but also on a data governance framework, including policies, processes, and controls. Training effective AI models relies heavily on having high-quality, well-maintained data - so invest in resources to improve your data management.
2. Develop your strategy using design-thinking - start by properly understanding the needs of key users, such as scientists, clinicians, patients, and operations teams. Consider their biggest pain points, inefficient processes, and opportunities to improve their outcomes and experiences. Then, prioritise AI applications that directly address and solve these identified needs.
3. Start with small, focused proof-of-concept projects - in order to build AI skills. Focus on applications with high impact, but take an agile approach and refine quickly using feedback. Collaborating with subject matter experts is crucial to ensure AI models are accurate and unbiased. This can be done by setting up an AI Centre of Excellence - a unit within your business that is responsible for AI best practices.
4. Establish an AI policy early on to create a culture that welcomes this new technology, and in a way that ensures responsible use through principles of transparency, fairness, privacy, and ethics. Treat AI as a tool to complement human expertise, not to replace it.
While the possibilities of AI can seem daunting, the companies that can harness AI's power while ensuring they stay human-first, will be the ones who gain a competitive edge.
By starting with high-impact use cases and building the right data and ethical foundations to scale successfully from the outset, life sciences leaders can guide their organisations to embrace this change.
Ready to start embracing AI in life sciences?
We partner with industry leaders in life sciences and healthcare to deliver cutting-edge AI solutions, and accelerate digital transformation and capabilities.
See how we enabled AI-powered genetic sequencing for a leading biotechnology company here.
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