Improving Automating Quality Control in Radiology: Leveraging Large Language Models to Extract Correlative Findings in Radiology and Operative Reports
Recommended Citation
Eghbali N, Klochko C, Razoky P, Chintalapati P, Jawad E, Mahdi Z, Craig J, and Ghassemi MM. Improving Automating Quality Control in Radiology: Leveraging Large Language Models to Extract Correlative Findings in Radiology and Operative Reports. AMIA Jt Summits Transl Sci Proc 2024; 2024:135-144.
Document Type
Article
Publication Date
1-1-2024
Publication Title
AMIA Jt Summits Transl Sci Proc
Abstract
Radiology Imaging plays a pivotal role in medical diagnostics, providing clinicians with insights into patient health and guiding the next steps in treatment. The true value of a radiological image lies in the accuracy of its accompanying report. To ensure the reliability of these reports, they are often cross-referenced with operative findings. The conventional method of manually comparing radiology and operative reports is labor-intensive and demands specialized knowledge. This study explores the potential of a Large Language Model (LLM) to simplify the radiology evaluation process by automatically extracting pertinent details from these reports, focusing especially on the shoulder's primary anatomical structures. A fine-tuned LLM identifies mentions of the supraspinatus tendon, infraspinatus tendon, subscapularis tendon, biceps tendon, and glenoid labrum in lengthy radiology and operative documents. Initial findings emphasize the model's capability to pinpoint relevant data, suggesting a transformative approach to the typical evaluation methods in radiology.
PubMed ID
38827099
Volume
2024
First Page
135
Last Page
144
