Kinki University Hospital's Department of Oncology (hereinafter, Kinki University Hospital), Chugai Pharmaceutical Co., Ltd. (hereinafter, Chugai Pharmaceutical), NTT Corporation (hereinafter, NTT), and NTT DATA Corporation (hereinafter, NTT DATA) have launched a four-party joint research project (hereinafter, this research) in June 2026 to verify the accuracy and efficiency of the clinical trial patient recruitment process using real-world data accumulated in actual clinical practice and large language models (LLMs), an AI technology. In this research, Kinki University Hospital's electronic health record data will be used as the target, and the extraction methods combining the conventional approach with LLMs will be compared and evaluated based on the eligibility criteria defined in the clinical trial protocols formulated by Chugai Pharmaceutical. Using the judgment results of physicians and clinical research coordinators (CRCs) as a benchmark, the effectiveness in actual operation, reduction in workload, and contribution to shortening the lead time until patient enrollment will be comprehensively verified. [Background] In the clinical development of new drugs, the period until the start of a clinical trial and the enrollment period for trial participants significantly impact the time to market. In particular, the extraction of potential clinical trial patients requires physicians and CRCs to individually review patient information based on the eligibility criteria defined in the clinical trial protocol, which has historically demanded significant time and effort. As a result, it has been pointed out that patient enrollment often does not proceed as planned, affecting the overall trial schedule. In recent years, with the increasing utilization of real-world data accumulated in actual clinical practice, the application of LLMs, which can interpret patient information, including unstructured data, across various sources, has garnered attention. By leveraging these te