Owing to their capacity to analyze rich and complex interconnected data, Graph Neural Networks (GNNs) enabled novel opportunities in clinical practice. GNNs are the topic of concern of this chapter because they help solve the gap between research ideas and real-world aspects in healthcare. With the GNN’s capacity to analyze relationships and interactions between elements that are formulated as networks, researchers will be able to tackle the major issues of medical diagnosis, treatment strategies, and individualized medicine. The chapter includes a broad overview of the major GNN concepts and their benefits over other non-GNNs in handling multimodal and longitudinal data common in clinical and health care settings. GNNs’ advantages in representing patient networks, disease frameworks, and biochemical connections are also noted. With real-world cases, we show how GNN applications permit modeling of disease evolution, classification of patient populations, and searching for new drug targets that suppress diseases. During the course of the research, GNNs were implemented with clinical usability in mind. This encompasses utilizing them across radiology, genomics, and electronic health records (EHRs) for improved imaging, biomarker identification, and predicting patient outcomes respectively. Besides, the chapter features how GNNs make drug design activities faster by simulating action mechanisms of drugs as well as in drug repurposing. Encouraging as they may be, the chapter equally considers challenges in adopting these tools in clinical settings, including data homogenization, interpretation, and abiding by regulatory requirements. Approaches to overcoming some of these challenges such as developing explainable AI tools and integrating computational scientists and clinicians are discussed. Finally, after deliberating over the future application of GNNs in the field of medicine, the chapter underscores the place of GNNs in the provision of precise medications in order to achieve better patient outcomes. Furthermore, through collaboration between clinicians and computational scientists, GNNs have the potential to change the way in which care is provided. This will ensure that there is continuity between the research and practice aspects of medicine.