By Roger Lam, MBX Systems
The rise of artificial intelligence and edge computing has paved the way for computer vision applications such as fall detection, virtual patient interactions and remote surgery viewing designed for deployment near hospital beds, in operating rooms and in other edge environments. Until recently, however, most initiatives in this area have been stymied by lengthy and expensive development cycles.
Independent software vendors (ISVs) aiming to develop these kinds of smart hospital solutions had to spend up to two years and $100,000+ for custom medical cart design, engineering and mold injection, and nearly that long to build machine learning models from scratch. By the time the finished solution was ready for market, technology advances such as CPU and GPU performance gains likely made it obsolete, leaving vendors with unsellable prod…