Artificial Intelligence/Machine Learning

Clinical R&D modernization stalls through incremental optimization of individual workflows, but meaningful systemic change requires leaders to visualize structural relationships, understand hidden incentives, and identify leverage points that benefit the whole system rather than parts.

The traditional global-to-local communication model creates fragmented content through sequential adaptation, but a local-first approach using structured data and AI-orchestrated generation can embed stakeholder terminology and preferences from the start, reducing rework while improving clarity and relevance.

Eligibility criteria alone rarely predict enrollment, which depends more on trust, hope, and fear, requiring relationship-based community engagement paired with behavioral readiness assessments rather than transactional outreach.

AI-enabled systems medicine could influence which populations are selected for trials, how biomarkers are validated, how products are positioned, how evidence is generated after launch, and how value is demonstrated to payers.

In this Q&A, Liz Beatty, co-founder and chief strategy officer at Inato, discusses how real-time patient data is reducing non-enrolling sites, why sponsor-specific technology remains the biggest barrier to adoption, and what a shift toward cross-asset site partnerships could mean for enrollment efficiency.

The FDA's launch of real-time clinical trial proof-of-concept studies signals a fundamental shift in regulatory oversight, one that most sponsors are not yet equipped to meet and that demands urgent investment in unified data infrastructure, quality-by-design practices, and protocol digitization.

In this Q&A, Raj Indupuri, CEO and co-founder of eClinical Solutions, discusses what the FDA's push toward continuous data review actually demands of sponsors operationally, why fragmented systems are the core obstacle, and how AI and real-world evidence fit into a more data-driven regulatory environment.

From real-time evidence generation to federated AI to site-level data integration, ACT spoke with seven experts at DIA 2026 on the trends and challenges defining clinical trial operations today.

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, 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, shares results from a Sanofi COPD study where AI-enabled sites screened patients 33% faster and achieved 100% enrollment success—including one site that had been non-performing for over 100 days.

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, describes how AI can take on rote reasoning tasks like PHI redaction and document QA, and why offloading that work is what gives human experts the clarity to focus on genuine decision making.

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.