Two stage large language model approach enhancing entity classification and relationship mapping in radiology reports.

ABSTRACT: Large language models (LLMs) hold transformative potential for medical image labeling in radiology, addressing challenges posed by linguistic variability in reports. We developed a two-stage natural language processing pipeline that combines Bidirectional Encoder Representations from Transformers (BERT) and an LLM to analyze radiology reports. In the first stage (Entity Key Classification), BERT model identifies and classifies clinically relevant entities mentioned in the text. In the second stage (Relationship Mapping), the extracted entities are incorporated into the LLM to infer relationships between entity pairs, considering actual presence of entity. The pipeline targets lesion-location mapping in chest CT and diagnosis-episode mapping in brain MRI, both of which are clinically import

SUBMITTER: Shin C 

PROVIDER: S-EPMC12391506 | biostudies-literature | 2025 Aug

REPOSITORIES: biostudies-literature