AI-Enabled Standardization for Scalable Cell & Gene Therapy Trial Matching

AI-Powered Patient Identification for Advanced Cell & Gene Therapy Clinical Trials Advanced cell and gene therapies—including CAR-T therapies, bispecific antibodies, and adoptive cellular therapies—are reshaping the treatment landscape in oncology. However, identifying eligible patients for these innovative therapies and the clinical trials evaluating them remains a major challenge. Much of the information required to determine eligibility is embedded within unstructured electronic health records (EHRs), making manual review labor-intensive, inconsistent, and difficult to scale. In this webinar, discover how a fine-tuned GPT-4o platform integrated into an agentic AI workflow can transform unstructured oncology records into standardized, mCODE 3.0-compliant patient profiles. By automating clinical data extraction and standardization, this approach enables rapid patient pre-screening, improves clinical trial matching, and reduces the administrative burden on research and clinical teams. You'll also explore validation results demonstrating the performance of AI-driven patient identification and learn how this technology can accelerate enrollment into advanced cell and gene therapy clinical trials while expanding patient access to potentially life-changing treatments. Key Learning Objectives Understand the challenges of identifying eligible patients using unstructured oncology EHR data. Learn how GPT-4o and agentic AI extract and standardize clinical data using the mCODE 3.0 framework. Discover how AI automates patient pre-screening and clinical trial matching. Review validation results demonstrating the accuracy and effectiveness of AI-powered data extraction. Explore how AI can improve clinical trial enrollment efficiency and increase patient access to advanced cell and gene therapies.