This systematic review explores how Natural Language Processing (NLP) technologies can automate and improve the typically labor-intensive process of matching patients to clinical trials.

• Analyzes cutting-edge approaches for processing both unstructured clinical text and structured EHR data
• Identifies methodologies that reduce manual screening effort while improving matching accuracy
• Highlights challenges in handling complex eligibility criteria and medical terminology
• Maps the evolution of NLP applications in clinical trial recruitment

This research matters because efficient eligibility matching can significantly accelerate medical research timelines, reduce recruitment costs, and ensure more representative patient populations in clinical studies.

Systematic Literature Review on Clinical Trial Eligibility Matching