Clinical research underpins medical progress, yet conventional trials face major barriers such as recruitment difficulties, high costs, and long timelines. Globally, the average randomized controlled trial (RCT) takes over 6 years to complete, with a 40% failure rate, often due to inadequate sample sizes and poor adherence . In China, where the volume of trials is rapidly expanding, issues of efficiency and quality are increasingly evident, with large multicenter studies frequently delayed by patient management and data integration bottlenecks. Addressing these global and local challenges requires next-generation AI solutions.
Clinicopilot is an AI-driven intelligent trial agent developed by Deeplynx. By analyzing target population electronic medical records (EMRs) and health data, it generates digital twin individuals to form simulated study cohorts. This approach supports both quantitative research (e.g., outcome prediction, intervention comparison) and qualitative research (e.g., patient experience, adherence factors). Clinicopilot aims to accelerate study design, testing, and optimization while reducing risk and improving both scientific rigor and generalizability.
Clinicopilot operates through three core modules:
1. Data Modeling – NLP and ML parse structured and unstructured EMR data;
2. Digital Twin Generation – virtual patients are built from real-world characteristics, forming controllable cohorts;
3. Scenario Simulation – supports rapid testing of diverse trial scenarios (e.g., drug dosing, intervention combinations).
A qualitative simulation layer enables virtual patient dialogues, helping researchers better understand perceptions and barriers to optimize study designs.
Early applications suggest that Clinicopilot can reduce protocol optimization time by 50% and improve sample size and feasibility prediction accuracy by over 30%. In a COPD management pilot, the system simulated trial scenarios to predict recruitment rates, with results validated by real-world clinical trial data (OR=1.43). Findings from a hypertension management study also demonstrated significant improvements in trial efficiency. Globally, AI-enabled virtual twin trials have been shown to reduce trial failure rates by 20–30%, underscoring their potential as a transformative paradigm for clinical research.