Using AI & Machine Learning To Better Understand Data And Manage Risk

Against a backdrop of stricter regulatory standards and increased emphasis on trial oversight and patient safety, the clinical development landscape is more competitive than ever. Clinical trials have also increased in complexity, driven in large part by the shift toward biomarker-guided drug development and value-based outcomes. With about 7,000 medicines in development globally, nearly three-quarters of which have the potential to be first-in-class treatments,[1]we are living in an era of medical innovation that is on the verge of technological disruption.

Artificial intelligence (AI), and more specifically machine learning, has the potential to transform clinical trials — and healthcare in general — by deriving critical new insights from the vast amount of data generated during the course of healthcare delivery.[2]In January 2019, then-FDA Commissioner Dr. Scott Gottlieb stressed the importance of modernizing the clinical trial process by pairing real-world data with advances in machine learning, stating that, “new approaches and new technologies can help expand the sources of evidence that we use to make more reliable treatment decisions.[3]”