Privacy-Preserving Large Language Model for Matching Findings and Tracking Interval Changes in Longitudinal Radiology Reports.

ABSTRACT: In current radiology practice, radiologists identify a finding in the current imaging exam, manually match it against the description from the prior exam report and assess interval changes. Large Language Models (LLMs) can identify report findings, but their ability to track interval changes has not been tested. The goal of this study was to determine the utility of a privacy-preserving LLM for matching findings between two reports (prior and follow-up) and tracking interval changes in size. In this retrospective study, body MRI reports from NIH (internal) were collected. A two-stage framework was employed for matching findings and tracking interval changes. In Stage 1, the LLM took a sentence from the follow-up report and discovered a matched finding in the prior report. In Stage 2, the L

SUBMITTER: Mathai TS 

PROVIDER: S-EPMC12920831 | biostudies-literature | 2026 Feb

REPOSITORIES: biostudies-literature