Artificial Intelligence/Machine Learning

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.

As agentic AI, automated data harmonization, and real-time monitoring reshape clinical development, the organizations seeing meaningful results are those that have invested in unified data infrastructure and disciplined governance rather than cycling through pilots without the foundation to scale them.

AI agents operate only within workflows, but most organizations lack unified workflow management systems, making it difficult to identify where agents should be deployed or ensure they integrate effectively across connected business processes in clinical research.

AI churn—repeatedly restarting initiatives before scaling them—stems from organizational execution gaps rather than technology limitations, but agentic AI amplifies these gaps by requiring connected systems, trustworthy data, and disciplined governance from the start.

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.

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, 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 video interview, Raj Indupuri, CEO and co-founder of eClinical Solutions, argues that study-by-study RBM is no longer sufficient and describes what enterprise-wide, AI-enabled risk and quality management looks like in practice.

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.

In this video interview, Liz Beatty, co-founder and chief strategy officer at Inato, makes the case for moving beyond trial-by-trial planning toward cross-asset site partnerships—and explains why sites are three times more likely to share patient data when sponsors make that shift.

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 why asking sites to use different technology for every sponsor remains a major barrier to AI adoption and what a site-first approach to technology development looks like in practice.

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.