Towards AI-Assisted Clinical Trial Matching: Practical Considerations, Multicenter Evaluation, and Real-World Deployment

A research initiative, TrialGPT 2.0, addresses the critical challenge of insufficient patient enrollment in clinical trials, particularly in oncology, which often hinders advancements in drug development and care. This AI-assisted recommendation system moves beyond basic eligibility assessment to consider patient clinical needs and local workflow priorities, providing explainable recommendations for expert review. It has been designed for real-world deployment and multicenter evaluation.

Why it matters

This development is strategically important as it addresses a systemic bottleneck in medical research and drug development—patient recruitment. By leveraging AI to optimize trial matching, it can accelerate the pace of innovation, potentially leading to faster access to new treatments and more efficient allocation of research resources across the healthcare and pharmaceutical sectors.

Key insights

• Insufficient patient enrollment remains a significant obstacle in clinical trials for advancing cancer care and drug development.
• Existing AI systems for patient recruitment often focus solely on eligibility assessment.
• TrialGPT 2.0 is an AI-assisted clinical trial recommendation system developed for real-world deployment.
• The system assesses not only patient qualification but also which trials are most relevant based on current clinical needs and local workflow priorities.
• TrialGPT 2.0 provides structured and inspectable explanations to support expert review of its recommendations.
• The system's evaluation includes multicenter assessment and consideration of real-world oncology workflows.

Source

arXiv — Computers and Society — https://arxiv.org/abs/2609.01202