  In clinical artificial intelligence (AI), graph representation learning,
mainly through graph neural networks (GNNs), stands out for its capability to
capture intricate relationships within structured clinical datasets. With
diverse data -- from patient records to imaging -- GNNs process data
holistically by viewing modalities as nodes interconnected by their
relationships. Graph AI facilitates model transfer across clinical tasks,
enabling models to generalize across patient populations without additional
parameters or minimal re-training. However, the importance of human-centered
design and model interpretability in clinical decision-making cannot be
overstated. Since graph AI models capture information through localized neural
transformations defined on graph relationships, they offer both an opportunity
and a challenge in elucidating model rationale. Knowledge graphs can enhance
interpretability by aligning model-driven insights with medical knowledge.
Emerging graph models integrate diverse data modalities through pre-training,
facilitate interactive feedback loops, and foster human-AI collaboration,
paving the way to clinically meaningful predictions.
