The Application of Compressed Sensing in Clinical Disease Diagnosis and Future Directions

Compressed Sensing (CS) is an innovative signal processing technique that exploits the sparsity of signals to reconstruct them from fewer measurements than traditionally required by the Nyquist-Shannon theorem. Over the past decade, this method has gained significant traction in various fields, particularly in clinical disease diagnosis, where it has the potential to revolutionize imaging, signal processing, and data analysis. This review aims to provide a comprehensive overview of the current applications of CS in clinical settings, identify areas where further improvements are needed, and discuss future directions for research and development.