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Hierarchical composite endpoints analyzed through pairwise comparisons more accurately reflect multifaceted treatment benefit than time-to-first-event composites, but transparent outcome prioritization, patient involvement in ranking, and reporting of Net Treatment Benefit remain underutilized despite their importance to interpretation.

In this video interview, Claire Riches, VP of clinical solutions at Citeline, offers practical guidance for clinical operations teams beginning their AI journey—making the case that the tools are more accessible than many assume and that waiting to start is a competitive risk in itself.

In today's ACT Brief, we examine how AI enables sponsors to pressure-test protocols before enrollment, HHS's multi-initiative effort to accelerate trial design and execution, and FDA approval expanding heart disease therapy to adolescents.

The new effort combines adaptive platform trial design, AI-enabled site activation, nationwide data infrastructure, and patient data contribution tools to reduce timelines, costs, and patient burden across clinical development.

In this video interview, Claire Riches, VP of clinical solutions at Citeline, explains how AI is shifting trial risk management from reactive to proactive—enabling sponsors to pressure-test protocols and anticipate pivots before a single patient is enrolled.

In today's ACT Brief, we examine AI's role as trial design advisor with human leadership, how to govern autonomous agents in regulated operations, and real-world outcomes from switching to oral weight-loss therapy.

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.

In this video interview, Claire Riches, VP of clinical solutions at Citeline, makes the case for AI as a sophisticated strategic advisor in trial design while arguing that humans must remain in the lead—especially when factors the model can't fully account for are at stake.