Clinical Trial Matching

Clinical trial matching aims to efficiently connect patients with suitable clinical trials, a process currently hampered by the volume and complexity of medical data and trial eligibility criteria. Recent research heavily utilizes large language models (LLMs), such as GPT-4 and custom-trained variants, to automate this process, focusing on end-to-end pipelines that encompass both trial identification and patient eligibility assessment. These methods show promise in improving the speed and accuracy of matching, potentially increasing patient access to relevant trials and accelerating medical research, although challenges remain in handling real-world data complexities and mitigating potential biases. Further work is needed to refine these models and ensure equitable access for all patient populations.