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Abstract: Graph neural networks (GNNs) are a type of deep learning architecture that can process data organized in graphs. A node in the graph represents an entity and an edge explains its relationship with other entities. Traditional neural networks operate on data arranged in grids or sequences. GNNs can handle dynamic and irregular data structures. GNNs share their information with nodes in their immediate vicinity and promote the concept of links to nodes of any size. GNNs can help advance health care. They assist in interpreting molecular topology and interplay. By predicting the interactions between molecules and proteins and identify possible molecules, GNNs might be used to streamline the process of drug discovery. Virtual screening of large databases can be used to identify compounds that have greater potential, thus limiting the trial-and-error process. GNNs may help in diagnosis and treatment planning by modeling the relationships between various features and analyzing medical images. GNNs may also play a significant role in bioinformatics to assist in the analysis of gene regulatory networks and prediction of protein-protein interactions. It may also be applied to reuse drugs in different ways by analyzing compound-target interaction networks. Information siloes can be mitigated with the help of GNNs, because they can merge various data types into a single heterogeneous graph. GNNs are computationally expensive and slow compared with their simpler counterparts. However, this is offset by the lower cost of the experiments, capability to combine multi-modal data, and scalability owing to their effectiveness in the screening of ultra-large libraries of molecules. This chapter provides an overview of the application of GNNs in the healthcare domain and their potential, with illustrations of cases in which their benefits have been demonstrated.