Retrospective Comparative Analysis of Prostate Cancer In-Basket Messages: Responses From Closed-Domain Large Language Models Versus Clinical Teams.

ABSTRACT:

Objective

To evaluate the effectiveness of RadOnc-generative pretrained transformer (GPT), a GPT-4 based large language model, in assisting with in-basket message response generation for prostate cancer treatment, with the goal of reducing the workload and time on clinical care teams while maintaining response quality.

Patients and methods

RadOnc-GPT was integrated with electronic health records from both Mayo Clinic-wide databases and a radiation-oncology-specific database. The model was evaluated on 158 previously recorded in-basket message interactions, selected from 90 patients with nonmetastatic prostate cancer from the Mayo Clinic Department of Radiation Oncology in-basket message database in the calendar years 2022-2024. Quantitative natural language processing analys

SUBMITTER: Hao Y 

PROVIDER: S-EPMC11932704 | biostudies-literature | 2025 Mar

REPOSITORIES: biostudies-literature