Clinical development is full of tough decisions. Where to run a trial? Which patients to include? What’s the best design for the study? AI offers a way to challenge our biases and help us make the best decisions by rapidly integrating several facets, such as time, cost, probability of success, and the competitive environment. It also allows faster decision making by providing an unbiased assessment through data review and modelling that was not possible a decade ago.
When developing vaccines, one of the first challenges in clinical trials is figuring out how long it will take to recruit participants. This process, called feasibility, helps our researchers estimate realistic timelines, but it involves a lot of uncertainty. For example, to find patients with specific medical conditions or in certain age groups, we must predict the number of eligible participants in different locations. AI can analyze historical data and disease patterns for more accurate predictions, making recruitment more efficient and reliable.
“With vaccines, it’s crucial to predict where and how quickly a disease might spread, and how severe it could be,” explains Mornet. “AI transforms this process by giving us a clearer picture of key factors, such as where we can recruit and how many patients will be available, versus making educated guesses.”