  Electronic Health Records (EHRs) play an important role in the healthcare
system. However, their complexity and vast volume pose significant challenges
to data interpretation and analysis. Recent advancements in Artificial
Intelligence (AI), particularly the development of Large Language Models
(LLMs), open up new opportunities for researchers in this domain. Although
prior studies have demonstrated their potential in language understanding and
processing in the context of EHRs, a comprehensive scoping review is lacking.
This study aims to bridge this research gap by conducting a scoping review
based on 329 related papers collected from OpenAlex. We first performed a
bibliometric analysis to examine paper trends, model applications, and
collaboration networks. Next, we manually reviewed and categorized each paper
into one of the seven identified topics: named entity recognition, information
extraction, text similarity, text summarization, text classification, dialogue
system, and diagnosis and prediction. For each topic, we discussed the unique
capabilities of LLMs, such as their ability to understand context, capture
semantic relations, and generate human-like text. Finally, we highlighted
several implications for researchers from the perspectives of data resources,
prompt engineering, fine-tuning, performance measures, and ethical concerns. In
conclusion, this study provides valuable insights into the potential of LLMs to
transform EHR research and discusses their applications and ethical
considerations.
