 The transparent reporting of a multivariable prediction model of individual prognosis or diagnosis, tripod, statement and the prediction model risk of bias assessment tool, ProBust, were both published to improve the reporting and critical appraisal of prediction model studies for diagnosis and prognosis. This paper describes the processes and methods that will be used to develop an extension to the tripod statement, tripod artificial intelligence, AI and the ProBust, ProBust AI tool for prediction model studies that applied machine learning techniques. Tripod AI will provide researchers working on prediction model studies based on machine learning with a reporting guideline that can help them report key details that readers need to evaluate the study quality and interpret its findings, potentially reducing research waste. We anticipate ProBust AI will help researchers, clinicians, systematic reviewers and policy makers critically appraise the design, conduct an analysis of machine learning based prediction model studies with a robust standardized tool for bias evaluation. This article was authored by Gary S. Collins, Richard D. Riley, Lily Peng and others. We are article.tv, links in the description below.