Stop Guessing On Trial Feasibility: Simulating Patient Recruitment With AI

Designing a clinical trial protocol often means synthesizing vast amounts of fragmented information — from investigator brochures and prior clinical data to peer‑reviewed literature and emerging discovery insights. Turning that material into a feasible, regulator‑ready protocol is time‑consuming, and missteps early on can surface much later as recruitment failures or redesign requests.

This walkthrough demonstrates how an AI‑driven workflow brings structure and feasibility testing upstream. Large volumes of source material can be ingested securely and used to generate protocols aligned to company‑specific templates, including inclusion and exclusion criteria, endpoints, and supporting documentation for regulatory and ethics submissions. Importantly, synthetic patient data enables trial simulation before approvals are finalized. Teams can assess whether proposed criteria are realistic across geographies, forecast enrollment challenges, and evaluate statistical assumptions early.

Instead of discovering feasibility issues after a trial has launched, teams gain evidence to refine criteria, adjust endpoints, and engage regulators with data‑backed rationale. The result is a more practical, patient‑centered study design process — one that reduces downstream risk while improving confidence that approved trials can actually recruit, run, and deliver meaningful results.