When it comes to clinical development, saving time translates to saving lives, or at least improving them, through faster availability of treatments. For life sciences companies, it also translates into revenue opportunity—and a significant one at that. Some industry sources estimate that delivering new treatments to market ahead of schedule can be worth anywhere from $600,000 to $8 million per day.
The promise of speed is why generative AI is so compelling for life sciences companies. Generative AI helps automate standard development processes, find patterns, identify insights and produce relevant, meaningful content in any number of formats, all in a fraction of the time it would take for a human to do so.
We’ve identified four key areas of the clinical development lifecycle that can be accelerated and enhanced through the use of generative AI and supporting technologies. For life sciences companies that adopt gen AI, clinical development efficiency gains will be measured not in days or weeks but in months or even years—representing both an exponential revenue opportunity and renewed hope for patients.
Four ways to use gen AI in clinical development
#1: Streamline research processes using semantic search. Generative AI could fundamentally change the clinical and scientific research process. Instead of beginning with a manual keyword search and sifting through hundreds of articles across various sources, research teams could prompt a generative AI-enabled tool to rapidly search, gather and distill relevant articles or even suggest unanticipated information pathways to explore. This frees researchers to focus on analysis.
In our client engagements, we’ve seen 10X time savings when life sciences companies have applied generative AI to the research process.
Generative AI could also change how research is conducted. Because the underlying models understand intent and context, these tools can work from a mission- or goal-based prompt, as opposed to relying on traditional keyword searches.
For example, if a company is developing a cholesterol medication, a traditional process might begin with keyword searches involving different combinations of terms like "cholesterol," "clinical trial,” "efficacy," "safety," "hyperlipidemia" and "statins," ultimately uncovering disparate results across different source platforms.
However, with an AI-enabled tool, researchers could state their goal and receive contextualized reference materials to support that specific concept. “I'm designing a clinical trial for a new cholesterol medication,” a researcher might state to a generative AI tool. “Please provide an overview of the most recent studies on cholesterol medications, their efficacy and safety, and how they compare with existing treatments. Also, any insights on best practices and regulations would be valuable."
Finally, generative AI can also be used to analyze and synthesize relevant research materials and present them in a digestible way to a variety of constituents, from team members across multiple departments, to regulatory agencies, to institutional and ethical review boards. Ideally, these summaries would include a high degree of explainability regarding why those materials were selected. This further expedites the review process, which can also unlock time savings.