Large Language Model Applications for Health Information Extraction in Oncology: Scoping Review.

ABSTRACT:

Background

Natural language processing systems for data extraction from unstructured clinical text require expert-driven input for labeled annotations and model training. The natural language processing competency of large language models (LLM) can enable automated data extraction of important patient characteristics from electronic health records, which is useful for accelerating cancer clinical research and informing oncology care.

Objective

This scoping review aims to map the current landscape, including definitions, frameworks, and future directions of LLMs applied to data extraction from clinical text in oncology.

Methods

We queried Ovid MEDLINE for primary, peer-reviewed research studies published since 2000 on June 2, 2024, using oncology- and LLM-related keywo

SUBMITTER: Chen D 

PROVIDER: S-EPMC11970800 | biostudies-literature | 2025 Mar

REPOSITORIES: biostudies-literature