Artificial Intelligence/Machine Learning

In this Q&A, Claire Riches, VP of clinical solutions at Citeline, discusses how AI is transforming endpoint selection and protocol pressure-testing, what data inputs matter most for realistic enrollment planning, and why the tools are more accessible than most sponsors assume.

Radiopharmaceutical therapy is exposing a broader challenge for clinical development: scientific innovation can advance only as fast as the infrastructure, data, and cross-functional decisions required to deliver it.

AI agents in clinical operations acquire broader autonomous capability through expanded permissions, tools, memory, and delegated authority, requiring governance focused on whether effective capability has shifted outside approved boundaries rather than whether software has changed.

RWE is finding a role earlier in drug development than ever before, but the data quality, integration, and organizational alignment required to make it regulatory-grade are still catching up to the ambition.

In this Q&A, Andrea Valente, CEO of uMotif, discusses what it takes to build genuine trust between patients and sites, why making technology easier to use isn't the same as making patient-site interaction more effective, and where the industry is still falling short in its push toward patient-centered trial design.

Decentralized trial models have demonstrated real gains in enrollment performance and patient access, but operational switching costs, fragmented technology stacks, and unresolved gaps in the patient-site relationship raise a question the industry has been slow to confront directly.

Most AI pilots in drug development fail not from poor technology but from lack of strategic prioritization, organizational readiness, integrated data infrastructure, and disciplined governance, making success dependent on business discipline rather than technical capability.

A platform-based approach connects AI across workflows, data and governance, helping life sciences organizations move from isolated wins to real scale.

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.

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.

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.

Agentic AI is arriving in clinical operations with genuine capability to absorb administrative work, but the organizations that will realize lasting value are those willing to redesign accountability structures, financial workflows, and operating models before deploying the tools on top of them.

AI platforms that absorb trial data reconciliation could compress operations toward a small, accountable team.

Biomarker-informed trial enrollment appears straightforward for identifying responders and optimizing efficiency, but failures to account for demographic variation in cut-off selection, causality versus correlation, and composite biomarker complexity risk excluding viable patients and exacerbating disparities.

In this video interview, Patrick Mizer, chief technology officer at Ledger Run, addresses the reality that 80% of sites operate on six months or less of cash—and explains why growing investment in agentic AI and workflow modernization signals that the industry is starting to treat payment infrastructure as the priority it deserves.

AI is reshaping clinical research faster than any technology before it; but for sponsors and CROs, adoption without GxP-grade governance, validation, and human oversight isn't a strategy. It's a risk.

In this video interview, Patrick Mizer, chief technology officer at Ledger Run, describes how agentic AI is being applied to invoice processing—one of the most labor-intensive bottlenecks in site payments—and what meaningful improvement actually looks like in practice.

Agentic AI will put the CRO operating model to the test—and force the efficiency and accountability questions the industry can no longer avoid.

In this video interview, Amber Hill, PhD, founder and CEO of Research Grid, outlines the three conditions required for AI-driven trial acceleration to work at scale: broad AI literacy, shared boundaries around where it can be applied safely, and incremental adoption of purpose-built tools across the entire ecosystem.

In this video interview, Amber Hill, PhD, founder and CEO of Research Grid, explains why most trial failures are fundamentally administrative problems—and why patient sourcing, site feasibility, and back-office data capture are where AI delivers the fastest and most meaningful wins.