Automating Clinical Trial Enrollment: An AI Framework for Protocol Interpretation and Patient Matching
Keywords:
Artificial Intelligence, Clinical Trials, Protocol Matching, Regulatory ComplianceAbstract
The integration of Artificial Intelligence (AI) into clinical research is transforming the landscape of clinical trial operations. This study presents the development of an AI-driven system designed to facilitate patient-to-clinical trial matching by automatically interpreting clinical information and aligning it with patient health records. In addition, the system keeps up with all of the updates and changes to the standards and protocols governing the patient's information. While most studies focused on the use of structured data to determine eligibility to be included in the clinical trial, by utilizing a large Language Model (LLM), the proposed AI system will use unstructured data to derive clues from the data to determine eligibility. The proposed model aimed to reduce manual workload, expedite patient assignment, and ensure adherence to protocol guidelines. Furthermore, the proposed AI model underscores the potential of AI in advancing the precision and efficiency of clinical trial management and contributes to the broader movement toward digital transformation in medical research.
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