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OA

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.