  The incorporation of physical information in machine learning frameworks is
transforming medical image analysis (MIA). By integrating fundamental knowledge
and governing physical laws, these models achieve enhanced robustness and
interpretability. In this work, we explore the utility of physics-informed
approaches for MIA (PIMIA) tasks such as registration, generation,
classification, and reconstruction. We present a systematic literature review
of over 80 papers on physics-informed methods dedicated to MIA. We propose a
unified taxonomy to investigate what physics knowledge and processes are
modelled, how they are represented, and the strategies to incorporate them into
MIA models. We delve deep into a wide range of image analysis tasks, from
imaging, generation, prediction, inverse imaging (super-resolution and
reconstruction), registration, and image analysis (segmentation and
classification). For each task, we thoroughly examine and present in a tabular
format the central physics-guided operation, the region of interest (with
respect to human anatomy), the corresponding imaging modality, the dataset used
for model training, the deep network architecture employed, and the primary
physical process, equation, or principle utilized. Additionally, we also
introduce a novel metric to compare the performance of PIMIA methods across
different tasks and datasets. Based on this review, we summarize and distil our
perspectives on the challenges, open research questions, and directions for
future research. We highlight key open challenges in PIMIA, including selecting
suitable physics priors and establishing a standardized benchmarking platform.
