Slideshow: Patient Recruitment Factors in First-in-Man Studies

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.

In today's ACT Brief, we examine how data drives honest enrollment assumptions, why RWE infrastructure lags ambition, and the disconnect between FDA rulemaking and enforcement on compounded therapies.

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 why combining analog trial performance data with real-world patient data and site-level recruitment history gives sponsors a far more honest picture of whether their enrollment assumptions are actually achievable.