Generative Artificial Intelligence Successfully Automates Data Extraction From Unstructured Magnetic Resonance Imaging Reports: Feasibility in Prostate Cancer Care

Abstract

PURPOSE: Radiology reports are stored as plain text in most electronic health records, rendering the data computationally inaccessible. Large language models are powerful tools for analyzing unstructured text but relatively untested in urologic oncology. We aimed to develop a pipeline to extract data from plain text prostate magnetic resonance imaging (MRI) reports using GPT4.0 and compare the accuracy to manually abstracted data. METHODS: We developed a data pipeline using a secure, enterprise-wide deployment of OpenAI's GPT-4.0 to automatically extract data elements from prostate MRI report text when presented with prostate MRI reports. Identical prompts and reports were sent multiple times to determine response variability. We extracted 15 data elements per report and compared accuracy to a manually abstracted gold standard. RESULTS: < .001). In disagreements between manual and GPT-4.0 extracted data, GPT-4.0 responses were more often deemed correct by an additional reviewer. CONCLUSION: GPT-4.0 had high accuracy with low variability in extracting data points from prostate cancer MRI reports with low upfront programming requirements. This represents an effective tool to expedite medical data extraction for clinical and research use cases.

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