Research Paper

Core Innovation

This paper presents TrialGPT 2.0, which advances prior AI systems by not only assessing patient eligibility but also prioritizing trials based on clinical needs and local workflows with interpretable explanations, validated in real-world multicenter oncology settings.

Why It Matters

AI-assisted clinical trial matching addresses the critical bottleneck of patient recruitment in oncology, enhancing patient access to trials and reducing clinician workload. This scalable solution optimizes trial enrollment workflows across diverse care settings, accelerating cancer research and improving patient outcomes.

Potential Customers & Pain Points

• Cancer centers – Insufficient patient enrollment and lengthy trial matching processes
• Pharma companies – Inefficient patient recruitment causing trial delays
• Oncology clinicians – High workload screening trials manually
• Patients – Limited access to suitable clinical trials
• Health systems – Underutilization of trial opportunities.

Business Model

Enterprise SaaS subscription for health systems and cancer centers with premium support and integration services.

Competitive Landscape

• Flatiron Health's oncology trial matching tools; IBM Watson Health clinical trial matching solutions; Health Fidelity patient eligibility and trial recruitment platforms

Implementation Challenges

• Integration with diverse electronic health record systems across institutions; Securing clinician trust through transparent and explainable AI recommendations; Ensuring compliance with healthcare data privacy regulations such as HIPAA and GDPR

Validation Strategy

• Pilot implementation in multiple oncology centers to measure screening time reduction and increased trial enrollment rates; Technical validation through retrospective and prospective evaluations against clinician recommendations; Customer interviews to assess willingness to pay and refine user interface and explainability features