A Scalable AI Approach for Clinical Trial Cohort Optimization
This work addresses the problem of inefficient and non-scalable manual methods for clinical trial design, offering a scalable AI solution for researchers and regulators to improve trial diversity and generalizability, though it is incremental as it builds on existing NLP and data evaluation techniques.
The authors tackled the challenge of broadening eligibility criteria for clinical trials to enhance population diversity by proposing an AI approach (AICO) that uses transformer-based NLP and real-world data to optimize cohort design, demonstrating improved trial generalizability in a breast cancer case study.
FDA has been promoting enrollment practices that could enhance the diversity of clinical trial populations, through broadening eligibility criteria. However, how to broaden eligibility remains a significant challenge. We propose an AI approach to Cohort Optimization (AICO) through transformer-based natural language processing of the eligibility criteria and evaluation of the criteria using real-world data. The method can extract common eligibility criteria variables from a large set of relevant trials and measure the generalizability of trial designs to real-world patients. It overcomes the scalability limits of existing manual methods and enables rapid simulation of eligibility criteria design for a disease of interest. A case study on breast cancer trial design demonstrates the utility of the method in improving trial generalizability.