Harnessing Large Language Models for Clinical Information Extraction: A Systematic Literature Review
T. N. S. S. Rodrigues, Carla Teixeira Lopes · ACM Transactions on Computing for Healthcare · 2025
Electronic Health Records store extensive patient health data, playing a crucial role in healthcare management. Extracting information from these text-heavy records is difficult due to their domain-specific vocabulary, which challenges applying general-domain techniques. Recent advancements in Large Language Models (LLMs) and an increasing interest in the field have sparked considerable progress in solving Clinical Information Extraction (IE) tasks. We review these applications in Clinical IE, highlighting the most common tasks, most successful methods, and most used datasets and evaluation criteria. Examining 85 studies, we synthesize and organize the current research trends, highlighting common points between papers. The presence of LLMs can be felt in the most common tasks, with novel approaches being attempted and showing promising results. However, breakthroughs are still necessary in designing reliable end-to-end systems that can perform all the Clinical IE tasks within a single system.