Developing prompts from large language model for extracting clinical information from pathology and ultrasound reports in breast cancer.

ABSTRACT:

Purpose

We aimed to evaluate the time and cost of developing prompts using large language model (LLM), tailored to extract clinical factors in breast cancer patients and their accuracy.

Materials and methods

We collected data from reports of surgical pathology and ultrasound from breast cancer patients who underwent radiotherapy from 2020 to 2022. We extracted the information using the Generative Pre-trained Transformer (GPT) for Sheets and Docs extension plugin and termed this the "LLM" method. The time and cost of developing the prompts with LLM methods were assessed and compared with those spent on collecting information with "full manual" and "LLM-assisted manual" methods. To assess accuracy, 340 patients were randomly selected, and the extracted information by LLM metho

SUBMITTER: Choi HS 

PROVIDER: S-EPMC10556835 | biostudies-literature | 2023 Sep

REPOSITORIES: biostudies-literature