Interpretation of the TRIPOD-LLM reporting guideline for studies using large language models
- VernacularTitle:基于大语言模型的临床预测模型研究报告指南（TRIPOD-LLM）解读
- Author:
Xiaoqin ZHOU
1
;
Huizhen LIU
1
;
Ting WANG
1
;
Xuemei LIU
2
;
Deying KANG
1
Author Information
1. Center of Biostatistics, Design, Measurement and Evaluation (CBDME), Department of Clinical Research Management, West China Hospital, Sichuan University, Chengdu, 610041, P. R. China
2. Editorial Department of Chinese Journal of Clinical Thoracic and Cardiovascular Surgery, West China Periodicals Press of West China Hospital, Sichuan University, Chengdu, 610041, P. R. China
- Publication Type:Journal Article
- Keywords:
Large language model;
artificial intelligence;
predictive models;
reporting guideline;
TRIPOD-LLM;
interpretation
- From:
Chinese Journal of Clinical Thoracic and Cardiovascular Surgery
2025;32(07):940-946
- CountryChina
- Language:Chinese
-
Abstract:
As the volume of medical research using large language models (LLM) surges, the need for standardized and transparent reporting standards becomes increasingly critical. In January 2025, Nature Medicine published statement titled by TRIPOD-LLM reporting guideline for studies using large language models. This represents the first comprehensive reporting framework specifically tailored for studies that develop prediction models based on LLM. It comprises a checklist with 19 main items (encompassing 50 sub-items), a flowchart, and an abstract checklist (containing 12 items). This article provides an interpretation of TRIPOD-LLM’s development methods, primary content, scope, and the specific details of its items. The goal is to help researchers, clinicians, editors, and healthcare decision-makers to deeply understand and correctly apply TRIPOD-LLM, thereby improving the quality and transparency of LLM medical research reporting and promoting the standardized and ethical integration of LLM into healthcare.
