Review of physics-informed neural networks in hemodynamics

The circulatory system sustains physiological function through oxygen transport, nutrient delivery, and waste clearance, all of which rely on efficient blood flow. Accurate characterization and quantification of hemodynamics are essential for the diagnosis and treatment of cardiovascular diseases. However, assessing blood flow in a noninvasive and real-time manner remains a major challenge, as current imaging modalities often suffer from limited spatial and temporal resolution, while traditional computational fluid dynamics algorithms are computationally intensive and sensitive to anatomical and physiological uncertainties. Physics-informed neural networks (PINNs), combining physical laws with data-driven learning, provide a promising framework to connect computational modeling with clinical applications. In this review, we provide a comprehensive overview of recent advances in the application of PINNs to hemodynamics. We introduce theoretical foundations, highlight methodological innovations, and discuss applications in simulating blood flow under physiological and pathological conditions, as well as in estimating clinically relevant hemodynamic parameters. Importantly, our analysis highlights that PINNs achieve comparable accuracy to traditional methods while unlocking novel opportunities for patient-specific diagnosis and risk prediction. We conclude with a discussion of the benefits, current limitations, and future directions of PINNs in cardiovascular research, underscoring the transformative potential to accelerate clinical translation through interdisciplinary collaboration.