AIr - Artificial Intelligence Risk of bias tool (AIr)

Abstract

<ns3:p>Background The use of artificial intelligence or machine learning in the development of prediction models is increasing exponentially but the present model is associated with a high degree of heterogeneity and associated bias. The present model is associated with a difficult learning curve and we aimed to develop a tool evaluating risk of bias in cardiology research which was succinct and effective. Methods Our tool (AIr) consists of 10 questions and can be utilised to assess the risk of bias in model development, external validation, and the combination of the two in machine-learned or artificial intelligence models. Results AIr was as effective as the current risk of bias tool, PROBAST, however, was significantly more succinct and had a greater inter-rater reliability than PROBAST. Conclusion We propose that our tool maintains validity regarding the assessment of the risk of bias in cardiology publications whilst increasing reliability when compared with PROBAST.</ns3:p>

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