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Large language models like GPT-4.0 can accurately extract data from prostate MRI reports. This technology offers a powerful tool for streamlining medical data extraction in urologic oncology research.

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Area of Science:

  • Urologic Oncology
  • Medical Informatics
  • Artificial Intelligence in Medicine

Background:

  • Plain text radiology reports in EHRs hinder computational analysis.
  • Large language models (LLMs) show promise for unstructured text but are underutilized in urologic oncology.
  • Prostate MRI reports contain critical data for patient care and research.

Purpose of the Study:

  • To develop and evaluate a pipeline using GPT-4.0 for extracting data from prostate MRI reports.
  • To compare the accuracy of GPT-4.0 extracted data against a manual abstraction gold standard.
  • To assess the variability and accuracy of GPT-4.0 in a clinical setting.

Main Methods:

  • A secure enterprise-wide deployment of OpenAI's GPT-4.0 was used to process prostate MRI reports.
  • A data pipeline was designed to automatically extract 15 key data elements per report.
  • Response variability was assessed by sending identical reports multiple times; accuracy was compared to manual abstraction.

Main Results:

  • GPT-4.0 achieved consistently high accuracy (>95%) across 424 prostate MRI reports.
  • Individual data element accuracies exceeded 95% for key metrics like PSA density and TNM stage.
  • Response variability ranged from 0.14% to 3.61%, with higher accuracy correlating with lower variability.

Conclusions:

  • GPT-4.0 demonstrates high accuracy and low variability for extracting data from prostate cancer MRI reports.
  • The pipeline requires minimal upfront programming, making it an efficient tool.
  • This approach can expedite medical data extraction for clinical and research applications in urologic oncology.