Syneos Health combines AI-driven feasibility with curated global site networks to optimize country and site selection from the outset of each study. The result is a more strategic country mix, faster activation, and a better experience for both sites and patients.​ 

• AI-powered feasibility models ingest historical performance, epidemiology, and operational metrics to recommend the optimal blend of countries and sites for a given protocol.​ 
• Proprietary networks such as the Catalyst Site Network and DCT Site Network create a prequalified pool of high-performing sites across dozens of countries to reduce startup friction and support decentralized approaches.​ 
• Digital and decentralized tools (e.g., remote screenings, televisits, eCOA/ePRO, wearables) are layered into the country and site strategy to reach more diverse patients and support hybrid or fully decentralized designs.​ 

Syneos Health uses generative AI and structured authoring approaches to transform how regulatory, medical, and operational documents are created, while keeping scientific judgment and compliance under expert human oversight. These capabilities increase speed and consistency across trial protocols, medical data review outputs, and submission-ready documents.​ 

• Structured authoring frameworks standardize document organization, allowing writers to focus on high-value clinical and scientific content rather than formatting and duplication.​ 
• Gen AI tools generate first-pass drafts of key document types—such as protocols, safety narratives, stability summaries, and other regulatory components—within agentic workflows that maintain traceability and version control.​ 
• Human-in-the-loop review ensures that all AI-assisted content meets regulatory expectations (e.g., FDA, EMA) and aligns with internal quality standards, with medical writers and regulatory experts validating each deliverable.​ 

Syneos Health is deploying specialized, role-based AI agents that augment the work of clinical operations, medical monitoring, data review, and quality teams. These agents are designed to move oversight from reactive to predictive, continuously scanning trial data and workflows to surface the right insights to the right role at the right time.​ 

• Oversight agents monitor operational and clinical data in near real-time to flag quality issues, protocol deviations, and emerging risk signals, allowing monitors and study leaders to intervene earlier.​
• Medical data review agents assist clinicians with anomaly detection, signal prioritization, and cross-patient pattern recognition, improving both the speed and depth of medical review.​ 
• Role-specific agents for study startup, site management, and regulatory writing orchestrate tasks across systems, reducing manual workload and enabling teams to focus on strategic decision-making rather than routine processing.​