Artificial Intelligence/Machine Learning

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 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.

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.

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.

In this video interview from the 2026 DIA Global Annual Meeting, Angie Maurer, VP of AI-enabled clinical development at Medable, describes how digitizing protocols transforms manual amendment workflows into automated, AI-orchestrated processes—and why structured data from the start is the foundation the FDA's continuous review model depends on.

In this video interview from the 2026 DIA Global Annual Meeting, Stacy Hurt, chief patient officer at Parexel, explains how federated AI is expanding what's possible in oncology research, why the patient voice gets lost earliest in development, and why someone in every organization needs to explicitly own patient needs from the very beginning.

In this video interview from the 2026 DIA Global Annual Meeting, Angie Maurer, VP of AI-enabled clinical development at Medable, explains the three structural barriers that kept protocol digitization out of reach and why the convergence of LLMs, industry standards, and validated environments has finally changed the equation.

In this video interview, Raj Indupuri, CEO and co-founder of eClinical Solutions, describes how giving sponsors the ability to inject their own SOPs, protocols, and governance frameworks into AI systems is the key to building the trust needed for real-world adoption.