New Artificial Intelligence (AI) and machine learning techniques have the potential to improve prognostication in intensive care. However, they require further refinement before they can be introduced into daily practice.

This encompasses technical problems, such as uncertainty quantification, inclusion of more patient-centered outcome measures and important ethical issues notably regarding hidden biases as well as the transparency of data processing and the explainability of results.

Thereafter, AI models may become a valuable component of the intensive care team.

In addition to the standards of data stewardship in medicine, the selection of datasets and algorithms to create AI prognostication models must involve extensive scrutiny to avoid biases and, consequently, injustice against individuals or groups of patients.

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