Artificial Intelligence/Machine Learning

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.

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.

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.

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, makes the case that AI architecture—who built the model, who owns the data, and how outputs are traced—is the most important due diligence sponsors can do before adopting any AI tool in clinical development.

New collaborations with Causaly, Databricks, and Microsoft anchor a broader push to embed artificial intelligence directly into clinical trial decision-making and operational workflows.

In this video interview, Amber Hill, PhD, founder and CEO of Research Grid, identifies the administrative back-office processes causing the most friction in Phase I trials and explains why purpose-built, traceable AI models are the right tool for addressing them.

In this video interview, Amber Hill, PhD, founder and CEO of Research Grid, explains what hitting the 6- to 12-month timeline reductions envisioned under Operation TrialBlazer actually demands operationally—and where AI-native automation can make the biggest difference earliest.

In this Q&A, Raviv Pryluk, PhD, CEO and co-founder of PhaseV, discusses the data standardization bottleneck that sits between raw trial data and real-time analysis, what the FDA's continuous monitoring pilot will require to scale beyond large pharma, and why an ecosystem approach is the only path to making real-time trial oversight a practical reality.

Clinical research associates are evolving from compliance-focused manual monitors to data-enabled strategic site partners, but organizations need analytics training, clear operating models, and active change management to make the shift stick.

From point-of-care sample collection to real-world data curation, wearable device integration, and continuous review infrastructure, data quality is not a single problem in clinical trials—it is a layered challenge that manifests differently at every stage of development and demands a different set of solutions at each one.

New eNPV modeling applied to operational data from an AI clinical monitoring agent finds ROI multiples as high as 82x, with time savings and monitoring cost reductions identified as the primary value drivers.

In this Q&A, Richard Graham, PhD, co-founder and chairman of the board at TruTechnologies, discusses why six decades of manual site-level processes continue to undermine clinical trial execution, what national initiatives like Operation TrialBlazer leave unaddressed, and why meaningful modernization has to start with data collected at the point of care.

Real-time AI-enabled decision-making only accelerates development when trials have coordinated operational infrastructure for randomization, supply management, and workflows capable of responding to new insights without disrupting execution or compliance.

Under-enrollment isn't news to teams running oncology trials, and the instinct is to spend more on reach. The evidence, however, says the biggest leak is something else entirely and that most recruitment budgets focus on the least valuable part of the problem.

Real-world oncology data requires hybrid curation combining clinical expertise with technology to address variability in documentation, missing data, and complex concepts like lines of therapy that are often implicit rather than explicitly recorded in EHRs.