  Machine learning is increasingly recognized as a promising technology in the
biological, biomedical, and behavioral sciences. There can be no argument that
this technique is incredibly successful in image recognition with immediate
applications in diagnostics including electrophysiology, radiology, or
pathology, where we have access to massive amounts of annotated data. However,
machine learning often performs poorly in prognosis, especially when dealing
with sparse data. This is a field where classical physics-based simulation
seems to remain irreplaceable. In this review, we identify areas in the
biomedical sciences where machine learning and multiscale modeling can mutually
benefit from one another: Machine learning can integrate physics-based
knowledge in the form of governing equations, boundary conditions, or
constraints to manage ill-posted problems and robustly handle sparse and noisy
data; multiscale modeling can integrate machine learning to create surrogate
models, identify system dynamics and parameters, analyze sensitivities, and
quantify uncertainty to bridge the scales and understand the emergence of
function. With a view towards applications in the life sciences, we discuss the
state of the art of combining machine learning and multiscale modeling,
identify applications and opportunities, raise open questions, and address
potential challenges and limitations. We anticipate that it will stimulate
discussion within the community of computational mechanics and reach out to
other disciplines including mathematics, statistics, computer science,
artificial intelligence, biomedicine, systems biology, and precision medicine
to join forces towards creating robust and efficient models for biological
systems.
