Using Large Language Models to Translate Machine Results to Human Results

Abstract

Chest x-rays are among the most common diagnostic studies used in most both inpatient and outpatient settings, and they represent a significant portion of the workload for radiologists. Many different machine learning models have been developed for the analysis of chest x-rays, including models capable of detecting and labeling the location and type of pathological findings. In addition, large language models (LLMs) such as ChatGPT have also been growing in popularity and have proven to be effective at a variety of writing tasks [2]. For this project, we will attempt to use LLMs to translate machine learning results into automatically generated radiology reports. This would provide quick pre-reads of chest x-rays which can later be corrected or validated by radiologists in a similar workflow used by cardiologists when reading electrocardiograms (ECGs).

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