From Regulation to Data Aggregation: Three Machine Learning Trends to Watch

For over a decade, we've discussed the potential of machine learning (ML) in clinical research to objectively gather and analyze data, optimize trial design, and accelerate drug development. While the opportunities of these technologies get a lot of buzz, there is still a long way to go when it comes to proving they can deliver on their promise and ensuring their development is sustainable long-term. We now find ourselves at a crossroad to improve confidence in ML among pharmaceutical sponsors and clinicians, while finding alternative ways to keep pace with the data-hungry nature of these algorithms. Three key trends will direct the future of ML: regulatory guidance, an emphasis on model traceability as a means to build trust, and new data aggregation and analysis approaches that may help make ML innovation more practical and cost-effective. Until recently, federal oversight over ML's development has been limited, with developers defining best practices based on their own experience.