Generative-AI-Based Approaches for Information Extraction from Clinical Notes: A Scoping Review

Abstract

The growing adoption of Large Language Models (LLMs) in clinical settings could transform how information is extracted from clinical documents. Yet challenges persist regarding reliability, hallucinations, and data privacy. This scoping review examines 16 studies (2019-2025) to evaluate the efficacy of LLMs in extracting structured data from clinical practice guidelines and clinical notes, with a focus on prompt engineering strategies and model performance. Findings highlight GPT-4 as the top-performing model, leading in 11 out of 16 studies with >85% accuracy/F1-score in entity extraction. However, performance variability across document types and the necessity of privacy safeguards underscore the need for further research and ethical considerations before large-scale clinical deployment.

Ähnliche Arbeiten