Technology

In this Q&A, E.K. Koh, chief product officer at Suvoda, discusses what separates a trustworthy AI assistant from one that adds risk, why curated clinical knowledge matters more than general-purpose flexibility, and what safeguards must be in place when assistants move from answering questions to taking actions in clinical trial systems.

The FDA’s RTCT initiative transmits predefined, clinically meaningful signals to regulators during active trials, so its value depends entirely on the maturity of sponsors’ upstream RBQM signal intelligence, KRIs, QTLs, and root-cause context, rather than on data speed.

Medical writing capacity pressure is fundamentally a talent development problem, not a technology problem, since hybrid resourcing and AI cannot substitute for the experienced judgment needed to review AI output and mentor the next generation of writers.

Patients don’t experience primary endpoints, so trials that stop at biomarkers and rigid rating scales risk declaring success while leaving daily burden untouched, and digital tools only deliver meaningful evidence when they are co-designed with patients and deployed with restraint.

Standardized measurement across five oncology centers found that clinical research coordinators rated eSource-enabled workflows positively (Overall Mean Satisfaction 5.79 of 7), with reduced manual entry and electronic data transfer earning strong marks, while perceived reduction in sponsor and monitor queries lagged as the one domain below threshold.

In this Q&A, Saamir Pasha and Carole Berini, PhD, of Ontada discuss what it takes to harmonize fragmented oncology data into reliable real-world evidence, how lab markers can proxy for missing functional status data, and where the biggest remaining barriers to consistent RWE generation lie.

Clinical trial endpoints define what success looks like, but patients and caregivers are rarely the ones setting that definition, and the gap between what trials measure and what patients actually value is showing up in post-approval evidence, regulatory submissions, and the design rooms where protocols are written.

Real-time clinical trials require standardized digital data capture at the point of care through scaled eSource-EDC-EHR integration, but progress stalls because competing standards lack regulatory enforcement and one-off system integrations cannot scale beyond single studies without industry-wide alignment and legal clarity on data ownership.

Trialbee CEO Matt Walz explores the current impact of AI in delivering practical gains in clinical development and reflects on its future potential, where enrollment predictability may be the biggest prize of all.

Radiopharmaceutical therapy is exposing a broader challenge for clinical development: scientific innovation can advance only as fast as the infrastructure, data, and cross-functional decisions required to deliver it.

AI agents in clinical operations acquire broader autonomous capability through expanded permissions, tools, memory, and delegated authority, requiring governance focused on whether effective capability has shifted outside approved boundaries rather than whether software has changed.

RWE is finding a role earlier in drug development than ever before, but the data quality, integration, and organizational alignment required to make it regulatory-grade are still catching up to the ambition.

In this video interview, Claire Riches, VP of clinical solutions at Citeline, explains how AI is expanding endpoint selection beyond the bounded experience of a single sponsor team—and surfacing options that traditional design thinking might never have considered.

In rare disease drug development where no registry or natural history dataset exists, real-world evidence quality depends on treating evidence engineering as a design-stage decision, defining intended regulatory use and standardizing endpoints, harmonization, and data provenance upfront rather than reconciling gaps after collection.

In this Q&A, Andrea Valente, CEO of uMotif, discusses what it takes to build genuine trust between patients and sites, why making technology easier to use isn't the same as making patient-site interaction more effective, and where the industry is still falling short in its push toward patient-centered trial design.

Cell and gene therapy access expands into community care settings only when operational coordination, site readiness, and supply chain standardization become first-order priorities equivalent to manufacturing capacity, requiring standardized processes, digital integration, and distributed logistics networks.

In this episode of Beyond Compliance, Otis Johnson, PhD, MPA, founder and principal consultant at Vantix Operations, speaks with Cecilia Xi, PhD, VP of clinical and scientific affairs at Vivalink, about why defensible trial data depends on traceability, platform ownership, and device strategy long after a study ends.

Decentralized trial models have demonstrated real gains in enrollment performance and patient access, but operational switching costs, fragmented technology stacks, and unresolved gaps in the patient-site relationship raise a question the industry has been slow to confront directly.

Most AI pilots in drug development fail not from poor technology but from lack of strategic prioritization, organizational readiness, integrated data infrastructure, and disciplined governance, making success dependent on business discipline rather than technical capability.

In a video interview prior to the 2026 DPHARM conference, Andrea Valente, CEO of uMotif, explains why the real technology challenge isn't usability alone—it's making it easier for patients and sites to interact more effectively with each other.

In a video interview prior to the 2026 DPHARM conference, Andrea Valente, CEO of uMotif, shares what she expects to be at the center of the conversation—from building trust between patients and sites to where AI may and may not have a role in that relationship.

A platform-based approach connects AI across workflows, data and governance, helping life sciences organizations move from isolated wins to real scale.

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.