The demand for mobile and multitask devices has shifted the focus of research towards embedded systems and microcomputers.
These technologies provide relatively inexpensive solutions compared to heavy and cost-inefficient typical computing platforms. Microcomputers such as Jetson Nano or Raspberry Pi are designed for various tasks and can be interfaced with the majority of scientific and medical instruments, offering task automation and enabling them to accomplish a plethora of additional functions. Furthermore, the ability to use deep network models and produce real-time results (as in the case of Jetson Nano) can play a crucial role in medical applications.
Having multipurpose devices that can monitor temperature, capture images across the entire visual spectrum, utilize UV and NIR cameras, or even support radiometry instruments can be a valuable asset in current medical research and real-life applications. The significance of real-time data acquisition and processing on the Edge, using state-of-the-art purpose-built methodologies, is more relevant than ever. The future of this field is closely tied to innovation and cost minimization while producing the best possible results. This is why edge computing systems appear to be the way to go also for medical applications, combining compactness, cost-efficiency, and AI capabilities in a single power-efficient device.