Gen Li, PhD, MBA

How targeted AI can improve the performance of clinical trials.

The promise and acceptance of using this AI tool in drug development is growing.

Using existing clinical trial data to proactively model patient safety and efficacy outcomes could aid in accelerating development for areas in need of new therapies.

Already existing enrollment challenges emphasized by single patient sites.

Phesi and Tufts CSDD share results from research on operating practices that can be improved.

Digital twins, defined as a typical demographic patient profile in a specific population, have the potential to help clinical trial design.

Analysis of 330,000 clinical trials calls for improved protocol design process.

Analysis from Phesi shows the enrollment and effectiveness of two current COVID-19 vaccine trials conducted by global pharmaceutical development companies.

With the COVID-19 pandemic highlighting the existing issues with trial design and execution, the industry needs to address these problems now by becoming data-driven to ensure a long-lasting, positive impact on drug development and commercialization.

Case study shows that site activation is a key driver in determining patient enrollment cycle time, which can be minimized by identifying the "sweet spot" of sites to deploy for a clinical trial.

Case study demonstrates that site activation is a key driver in determining patient enrollment cycle time.

Building a structure for managing study operations through linking trial- and site-level forecasting.