06.11.26 -- AI Governance, Site Tech Risk, And Trial Efficiency Gains

DiMe’s sDHT Navigator aims to accelerate adoption, but timing and execution gaps still delay impact. Here’s what separates action from stalled pilots.

Experts unpack three tensions shaping trial tech: site reality vs. sponsor assumptions, integration vs. sprawl, and standardization vs. flexibility.

As FDA transitions Elsa’s AI models, legal experts outline how sponsors can safeguard confidential data, protect IP, and maintain regulatory compliance.

Regulators now expect auditability in AI-driven trials. Learn what governed infrastructure must deliver to ensure compliance and defensible outcomes.

Operational and cultural disconnects stall site digitization. Site-centered workflows can improve visibility, efficiency, and execution across trials.

Simulation enables teams to test assumptions early, improving feasibility, timelines, and cost control while supporting more confident trial design decisions.

Understanding IRT and RTSM differences helps sponsors optimize randomization, supply strategy, and trial integrity without getting lost in terminology.

Real-time workflows and flexible analytics improve data access, traceability, and collaboration, helping teams accelerate insights and decision-making.

Integrated data platforms with automation streamline collection and improve evidence quality, addressing inefficiencies in fragmented trial systems.

Patient-centric technologies improve access, engagement, and data quality, reshaping how trials recruit, monitor, and retain participants.

Patient communities are building research infrastructure and using AI to accelerate drug development, challenging traditional sponsor-led models.