Consensus statement on the credibility assessment of machine learning predictors

The rapid integration of machine learning (ML) predictors into in silico medicine has revolutionized the estimation of quantities of
interest that are otherwise challenging to measure directly. However, the credibility of these predictors is critical, especially when they
inform high-stakes healthcare decisions. This position paper presents a consensus statement developed by experts within the In Silico
World Community of Practice. We outline 12 key statements forming the theoretical foundation for evaluating the credibility of ML
predictors, emphasizing the necessity of causal knowledge, rigorous error quantification, and robustness to biases. By comparing ML
predictors with biophysical models, we highlight unique challenges associated with implicit causal knowledge and propose strategies
to ensure reliability and applicability. Our recommendations aim to guide researchers, developers, and regulators in the rigorous
assessment and deployment of ML predictors in clinical and biomedical contexts.