The clinical research industry talks about patient-centeredness constantly but embeds it too late, too narrowly, and without clear ownership, and the cost shows up in enrollment failures, protocol deviations, and outcomes that don't reflect what patients actually care about.

Why clinical development’s next AI bottleneck is not the model, but the system around it.

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.

In this Q&A, John Worden, chief commercial officer at Javara, discusses why clinical trial recruitment has remained stubbornly one-size-fits-all, how AI can identify patients at scale without displacing the clinician relationships that drive trust, and what care-integrated research engagement needs to look like for health systems, sponsors, and sites.

Participant engagement depends more on clarity and understanding than on gamification or incentives, requiring engagement embedded into trial workflows as a core design component rather than a peripheral consideration

In this episode of Beyond Compliance, Otis Johnson, PhD, MPA, founder and principal consultant at Vantix Operations, speaks with Joseph Geraci, PhD, co-founder and chief scientific and technical officer at NetraMark, about why explainable AI, not black-box prediction, is needed to reveal clinically meaningful patient subgroups in regulated drug development.

In this Q&A, Patrick Mizer, chief technology officer at Ledger Run, discusses how payment reliability has become a competitive differentiator in site selection, why decades of disconnected workflows have made payment inconsistency a structural problem, and where AI is delivering real value in the high-friction parts of clinical trial payments.

As sponsors push for faster, more representative studies, AI-enabled matching and EHR integration are emerging as operational levers, but bias, explainability, and governance will determine whether the technology scales.