Chinese interpretation of PROBAST+AI: An updated quality, risk of bias, and applicability assessment tool for prediction models using regression or artificial intelligence methods
- VernacularTitle:基于回归/AI的预测模型质量、偏倚风险与适用性评价工具更新版：PROBAST+AI中文解读
- Author:
Xingmeng WANG
1
;
Guohua DAI
2
;
Wulin GAO
2
;
Hui GUAN
2
;
Lili REN
3
;
Chen CHEN
1
;
Xiaoyang TAN
1
;
Yiming LIN
1
Author Information
1. First Clinical Medical College, Shandong University of Traditional Chinese Medicine, Jinan, 250014, P. R. China
2. Department of Geriatric Medicine, Affiliated Hospital of Shandong University of Traditional Chinese Medicine, Jinan, 250014, P. R. China
3. Department of Critical Care Medicine, Affiliated Hospital of Shandong University of Traditional Chinese Medicine, Jinan, 250014, P. R. China
- Publication Type:Journal Article
- Keywords:
PROBAST+AI;
artificial intelligence;
machine learning;
clinical prediction model;
systematic review
- From:
Chinese Journal of Clinical Thoracic and Cardiovascular Surgery
2025;32(12):1686-1695
- CountryChina
- Language:Chinese
-
Abstract:
The development and validation of clinical prediction models based on artificial intelligence (AI) and machine learning methods have become increasingly widespread. However, the prediction model bias risk and applicability evaluation tool developed in 2019 (i.e., PROBAST-2019) has shown significant limitations. Therefore, an expanded and updated version of the PROBAST-2019 tool was released in 2025, known as the PROBAST+AI tool. The tool is divided into two parts including model development and model evaluation. It aims to comprehensively and systematically evaluate potential methodological quality issues in model development, bias risks in model evaluation, and the applicability of models, regardless of the modeling method used. This paper provides a systematic interpretation of the PROBAST+AI tool's items and case analyses, with the aim of guiding and assisting researchers engaged in related studies and promoting the high-quality development of clinical predictive model research.
