  Machine learning methods in healthcare have traditionally focused on using
data from a single modality, limiting their ability to effectively replicate
the clinical practice of integrating multiple sources of information for
improved decision making. Clinicians typically rely on a variety of data
sources including patients' demographic information, laboratory data, vital
signs and various imaging data modalities to make informed decisions and
contextualise their findings. Recent advances in machine learning have
facilitated the more efficient incorporation of multimodal data, resulting in
applications that better represent the clinician's approach. Here, we provide a
review of multimodal machine learning approaches in healthcare, offering a
comprehensive overview of recent literature. We discuss the various data
modalities used in clinical diagnosis, with a particular emphasis on imaging
data. We evaluate fusion techniques, explore existing multimodal datasets and
examine common training strategies.
