医学新知2026，Vol.36Issue(7)：721-733,中插1-中插2,15.DOI:10.12173/j.issn.1004-5511.202601172
PROBAST+AI:基于传统或人工智能方法的预测模型研究质量、偏倚风险及适用性评价工具解读
PROBAST+AI:an interpretation of the tool for assessing the quality,risk of bias and applicability of prediction models based on traditional or artificial intelligence methods
摘要
Abstract
In recent years,artificial intelligence(AI)or machine learning(ML)methods have been increasingly used in the development and evaluation of clinical prediction models.Their algorithms differ from traditional regression modeling methods,resulting in significant limitations of the existing Prediction Model Risk of Bias Assessment Tool(PROBAST)for their evaluation.To address these limitations,the tool is updated to PROBAST+AI in 2025.PROBAST+AI extends the original framework to specifically address methodological challenges unique to developing or evaluating clinical prediction models based on AI/ML algorithms.PROBAST+AI assesses the quality of model development and the risk of bias in model evaluation across 4 domains:participants and data sources,predictors,outcome,and analysis,encompassing 16 and 18 signaling questions,respectively.Furthermore,the tool evaluates the applicability of the model across 3 domains:participants and data sources,predictors,and outcome.This article aims to compare the changes between the original and updated versions of the PROBAST tool,interpret the key content and items of PROBAST+AI,and apply the updated tool to evaluate an example clinical prediction model publication,to help domestic systematic review authors,clinicians,and policymakers critically appraise studies that develop or evaluate prediction models based on traditional or AI/ML methods.关键词
PROBAST+AI/预测模型/系统评价/偏倚风险/人工智能/机器学习Key words
PROBAST+AI/Prediction model/Systematic review/Risk of bias/Artificial intelligence/Machine learning分类
医药卫生引用本文复制引用
邹汶廷,郑起程,黄桥,王永博,任相颖,肖时烽,靳英辉,阎思宇..PROBAST+AI:基于传统或人工智能方法的预测模型研究质量、偏倚风险及适用性评价工具解读[J].医学新知,2026,36(7):721-733,中插1-中插2,15.基金项目
国家自然科学基金面上项目(82174230) （82174230）
国家自然科学基金青年科学基金项目(82505373) （82505373）
