Clinical trials have spent decades perfecting data capture, but the execution layer underneath it, including decision workflows, data collection design, and lab connectivity, remains fragmented in ways that limit what AI can realistically deliver and that quietly compromise the scientific validity of the data itself.
Clinical research has built sophisticated infrastructure for capturing and storing trial data, but the workflows, design decisions, and connective architecture that determine whether that data is trustworthy and actionable have lagged behind. Artificial intelligence (AI) is arriving on top of a foundation that, in many places, has not yet been completed.
