Zhonghua Liu Xing Bing Xue Za Zhi. 2026 May 10;47(5):958-964. doi: 10.3760/cma.j.cn112338-20250903-00624.
ABSTRACT
The prediction model risk of bias assessment tool (PROBAST) has made it difficult to assess the risk of bias in predictive models. In artificial intelligence (AI), this paper aims to interpret the PROBAST+AI: model development assessment with 16 targeted questions measuring model quality and applicability; and model validation with 18 signalling questions assessing risk of bias and applicability. Both phases cover four domains: study population and data sources, predictor variables, outcomes, and statistical analysis methods. The applicability evaluation was used for the three domains of study population and data sources, predictor variables, and outcomes. This paper helps researchers better understand and apply PROBAST+AI by comparing it with PROBAST 2019, thereby enhancing the transparency, credibility, and scientific value of AI modelling studies.
PMID:42151078 | DOI:10.3760/cma.j.cn112338-20250903-00624