Artificial Intelligence/Machine Learning

AI can improve recruitment only when it is embedded in protocol design, EHR-enabled matching, patient engagement, site workflow, and governance. The highest-value near-term use cases are human-in-the-loop decision-support applications with documented context of use, validation, privacy controls, and bias monitoring.

In this Q&A, Abraham Gutman, founder and CEO of AG Mednet, discusses why the clinical trial industry has mastered data capture but never built the execution architecture needed to act on it, how the right infrastructure changes the role of human experts, and why enthusiasm for agentic AI is outrunning what clinical trials can realistically support.

In this Q&A, Robert Hummel, chief operating officer at Suvoda, discusses how agentic AI is compressing RTSM build and deployment timelines, what safeguards are needed to maintain compliance and oversight at speed, and how intelligent automation will reshape the broader clinical trial technology stack over the next decade.

From rising costs and regulatory uncertainty to persistent vaccine hesitancy, sponsors face mounting pressure to standardize operations, build community trust, and develop the behavioral capabilities needed to run vaccine trials effectively in a rapidly shifting environment.

From planning one Phase III trial at a time to digital standardization on repeat.

As clinical trials grow increasingly complex and multi-modal, the pharmaceutical industry is pivoting toward AI-driven agentic orchestrators and lakehouse architectures to untangle disparate data streams, ensure regulatory compliance, and accelerate time-to-insight.

In this video interview, Liz Beatty, co-founder and chief strategy officer at Inato, explains how AI is replacing unreliable feasibility estimates with precise, real-time patient matching—and what that means for reducing non-enrolling sites and screen failure rates.

As the FDA formally recognizes real-world evidence as eligible confirmatory evidence for drug approval, sponsors face a growing imperative to build the data infrastructure, organizational alignment, and analytical capabilities needed to use RWE effectively across the development lifecycle.

In this video interview, Abraham Gutman, founder and CEO of AG Mednet, shares his key takeaways from SCOPE X, including a pointed caution against the idea that agentic AI can run clinical trials autonomously and why process architecture is the real entry point for AI to deliver on its promise.

In this video interview, Abraham Gutman, founder and CEO of AG Mednet, explains why decades of progress in data capture have not solved the execution problem in clinical trials, and what an operational architecture for AI actually looks like in practice.

Clinical Trials Day is an international celebration of everyone who makes medical discoveries possible. It is also an opportunity to shine a light on the innovations helping to keep research rising.

In this video interview, Sam Hinsley, statistics manager at Phastar, explains how statisticians can ensure patient data is used responsibly and innovatively across every phase of development, from rare disease to personalized medicine to AI.

Clinical development productivity improved in 2025, but gains remain fragile as end-to-end timelines lengthened again, signaling that future success depends less on individual trial execution and more on program-level orchestration, site engagement, and adaptive operating models.

From payment delays and feasibility misalignment to technology burden and AI adoption, clinical research sites are navigating a convergence of pressures that increasingly determine who sponsors work with and how well trials perform.

In this Q&A, Mark Freitas, managing director and life sciences practice lead at Alvarez & Marsal, discusses how clinical trial design has become a strategic business decision—and why small and midsize companies bear the greatest consequences when those decisions go wrong.

In this video interview, Mark Freitas, managing director and life sciences practice lead at Alvarez & Marsal, identifies governance inertia and review cycle lag as the most underappreciated risks when AI speeds up protocol development faster than organizations can act on the insights it generates.

In this video interview, Mark Freitas, managing director and life sciences practice lead at Alvarez & Marsal, offers a measured look at where AI is genuinely accelerating upstream protocol work and why proof points of faster approvals reaching patients are still limited.

The execution translation gap—the failure to convert identified problems into coordinated, timely action—costs millions per trial through delayed amendments, persistent deviations, and slow site activation, yet remains addressable through aligned accountability and proactive execution management.

In this Q&A, Krishna Cheriath, VP and head of clinical research digital data and AI at Thermo Fisher Scientific, examines how AI is reshaping clinical operations—from case intake and trial design to site burden reduction and the emerging reality of agentic AI in the workforce.

In this video compilation, industry experts share their perspectives on the operational, technological, and methodological shifts defining clinical research in 2026.

Pharma modernization initiatives stall not from lack of ambition but from expanding governance layers that distance leadership from execution, slowing decision velocity and delaying the systems integration that drives competitive advantage.

In this video interview, Krishna Cheriath, vice president and head of clinical research digital data and AI at Thermo Fisher Scientific, introduces a practical augmentation scale for thinking about how AI agents will transform clinical trial roles over the next two years and why workforce planning for that shift needs to start now.

In this video interview, Krishna Cheriath, vice president and head of clinical research digital data and AI at Thermo Fisher Scientific, maps the highest-impact opportunities for AI across the trial lifecycle—from smarter protocol design and enrollment matching to data collection, cycle time compression, and the emerging potential of synthetic data in rare disease.