This chapter explores the transformative role of artificial intelligence (AI) and large language models (LLMs) in improving the selection and recruitment of clinical trial participants. It discusses how AI-driven systems can streamline trial matching by automating the interpretation of complex eligibility criteria, integrating real-time electronic health record (EHR) data, and continuously synchronizing with trial registries such as ClinicalTrials.gov . Proprietary models like ChatGPT-4 and open-source frameworks including LLaMA, BioBERT, and Clinical BERT are evaluated for their performance in trial-patient matching, entity recognition, and entailment modeling. Recent innovations, such as TrialGPT, DeepEnroll, and OncoLLM, have demonstrated promising improvements in precision, recall, and processing efficiency, narrowing the gap between human expertise and algorithmic decision-making. The chapter also examines key comparative studies of proprietary and open-source systems, addressing critical issues such as scalability, cost, data security, and model transparency. While these technologies offer significant potential to increase recruitment efficiency and diversity, challenges persist regarding validation, ethical governance, and integration into clinical workflows. Ultimately, AI-powered clinical trial matching represents a pivotal advancement toward equitable, data-driven, and patient-centered clinical research.