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  1. Home
  3. Zero-shot Thoracic Oncologic History Generation For Radiologists Using Retrieval-augmented Large Language Model Pipeline.
  1. Home
  3. Zero-shot Thoracic Oncologic History Generation For Radiologists Using Retrieval-augmented Large Language Model Pipeline.

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Zero-shot Thoracic Oncologic History Generation for Radiologists Using Retrieval-augmented Large Language Model

Karan Jani1, Govind Mattay1, Vamsi Narra1

  • 1Mallinckrodt Institute of Radiology, Washington University School of Medicine, 510 S Kingshighway Blvd, St Louis, MO 63110.

Radiology
|June 16, 2026

View abstract on PubMed

Summary
This summary is machine-generated.

A new zero-shot large language model (LLM) pipeline accurately summarizes oncologic histories from electronic health records. This automated approach significantly reduces summarization time compared to manual methods, improving efficiency in oncology.

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Area of Science:

  • Artificial Intelligence in Medicine
  • Oncology Informatics
  • Natural Language Processing

Background:

  • Gathering oncologic history is crucial but inefficient in clinical practice.
  • Current large language model (LLM) summarization methods require manual effort and subjective evaluation, hindering clinical application.
  • Developing automated, objective methods for generating clinical summaries is essential.

Purpose of the Study:

  • To develop and evaluate a zero-shot LLM pipeline for programmatic generation of structured oncologic histories.
  • To utilize retrieval-augmented generation to ground LLM outputs for improved accuracy and reliability.
  • To assess the performance of different LLMs in summarizing oncologic history data.

Main Methods:

  • Retrospective electronic health record data from thoracic oncology patients were analyzed.
  • A retrieval-augmented generation pipeline was implemented to filter clinical data for summarization.
  • Three HIPAA-compliant LLMs (GPT-4o mini, o3-mini, GPT-5-mini) generated structured summaries using a zero-shot prompt approach.

    • Main Results:

    • The GPT-5-mini pipeline achieved the highest mean completeness score (95.5%) and accuracy (97.9% with o3-mini).
    • GPT-4o mini demonstrated the fastest processing time per summary (12.7 seconds), significantly below the 240-second manual benchmark.
    • Projected time savings could lead to substantial annual revenue increases per radiologist with minimal associated costs.

    Conclusions:

    • The developed LLM pipeline accurately summarizes oncologic history without manual fine-tuning or information retrieval.
    • The completeness and speed of automated summaries vary depending on the specific LLM employed.
    • This automated approach offers a promising solution for efficient and accurate clinical summarization in oncology.