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Generative AI is Revolutionising Life Sciences, And We’re Just Scratching the Surface

On average, it takes about seven years to develop a new drug and bring it to market. For ambitious life science businesses, generative AI’s ability to generate insights and content in a fraction of the time of a human means wiping months, or even years, off that average. When it comes to clinical development, saving time translates to saving lives, or at least improving them, through faster availability of treatments. It also translates into revenue opportunity – and a significant one at that. Some industry sources estimate that bringing new treatments to market ahead of schedule can be worth between £500,000 to £6.5 million per day. However, due to uncertain regulatory landscapes, coupled with the rapidly evolving nature of the technology, some companies are taking a wait-and-see approach to adopting generative AI, delaying investments until the course forward is clearer. While this may seem a prudent approach, it will be one organisation’s regret in the long run as they miss out on the opportunities generative AI holds, such as drug discovery and speed to market, and as competitors jump ahead and take the lead.

For life sciences companies looking to seize a competitive edge and supercharge their speed to market, there are a few key areas of the clinical development lifecycle they should focus on first.

Simplifying the Research Process

Research and development (R&D) is often the most time-consuming part of the drug development process, but AI can accelerate this process by up to 50% as the technology has a multiplier effect wherever it is applied.

Life sciences can implement generative AI at the very beginning of the R&D cycle, to aid in searching and synthesising available literature on a specific potential drug. Instead of beginning with a manual keyword search and sifting through hundreds of articles across various sources, teams could prompt a generative AI-enabled tool to rapidly search, gather and distil relevant articles – or even suggest unanticipated information pathways to explore.

Generative AI also has the potential to change how researchers find existing literature. Usually, researchers simply type keywords into the search box. But with a generative AI tool, they could state their goal into the prompt, providing context and intent, for the technology to find reference materials to support that specific ask, saving significant time while broadening the research horizon.

Automating Clinical Trial Protocol Writing

Compiling a clinical trial protocol document is a lengthy process that can take anywhere from a few months to over a year. Generative AI technology’s capabilities can automate a substantial proportion of the protocol writing process, bringing it down to days or even mere hours.

Generative AI can be trained on thousands of existing protocols in industry databases and each company’s own research data in order to identify the patterns relevant to investigational products, certain conditions, specific patient populations, or other factors. As the generative AI tool identifies relevant patterns, it can combine all the insights to design a baseline study, with a defined narrative that determines eligibility, drafts exclusionary criteria, and provides other necessary details. It can generate a number of draft options that would later be evaluated and refined by a human.