DescriptionFor designing personalized treatment strategies, measurable quantities (biomarkers) that relate a patient’s clinical representation to the existence, progress, and outcome of the disease need to be measured. They can often be formulated as quantities coming from biophysical models involving, for example, material deformations or fluid transport. However, the computational cost of numerically solving for these quantities can be prohibitive. These challenges are limiting the potential clinical impact of classical computational approaches, thus posing the need for new frameworks that reduce the time to prediction without sacrificing the physical consistency and fidelity of the inferred biomarkers. The success of machine learning methods provides a viable path to amortize the cost of these expensive simulations by training models to replicate the input-output behavior of the classical simulations. In purely data driven approaches, large amounts of labeled data are needed to train the model without leveraging any prior knowledge about the underlying biophysics. Unfortunately, in many biological scenarios the data acquisition process can be expensive and time consuming, limiting the amount of available training data. To address this difficulty, biophysics-informed machine learning offers a computationally efficient approach that has the potential to bridge the gap between modeling and clinical decision making.