Clinical text embeddings: A systematic review of methods, applications, and future directions

Clinical text embeddings are a foundational component of modern clinical natural language processing (NLP), which function by mapping high-dimensional, heterogeneous clinical texts into vector spaces. This process transforms diverse clinical texts into numerical vectors that capture underlying meaning, enabling different types of clinical texts to be analyzed and compared in a consistent way. The field has progressed with improvements made in general-domain NLP, moving from word-level embeddings to more advanced transformer-based models, and recently to LLM-based embeddings.

This review provides a systematic synthesis of various methods for clinical text embedding, and applications across clinical NLP tasks. For discussion, challenges that need to be resolved to successfully bring embedding-based methods into clinical practice are presented.

This study was conducted as a systematic review following the PRISMA 2020 guidelines. To comprehensively capture both clinical and computational perspectives of clinical text embeddings, literature from PubMed and DBLP published between January 1, 2017 and December 31, 2025 was considered. Out of an initial 17,644 records, quantitative filtering and eligibility criteria were applied to select 59 articles. Selected studies were synthesized into a taxonomy comprising three technical categories: Static Word/Document Embeddings, Contextual Transformer-Based Embeddings, and Knowledge-Enriched & Multimodal Embeddings, along with a survey of their clinical applications.

Static embeddings, while limited in context, remain valuable for their efficiency. Contextual transformer-based models have significantly advanced the field through domain-specific pretraining and instruction tuning. Knowledge-enriched and multimodal approaches have been found to further enhance performance by integrating structured knowledge graphs and EHR data. These techniques are actively applied across diverse clinical domains, powering tasks ranging from semantic search to patient risk prediction.

The paper concludes by proposing future directions necessary to achieve trustworthy, robust, and interoperable clinical text embeddings.