Reimagining Point-of-Care Ultrasound with Convolutional Neural Networks and Cloud Computing for Healthcare Transformation

Point-of-care ultrasound (POCUS) has become a vital device in healthcare, delivering fast diagnostic insights at the bedside. To transform POCUS, this paper's innovative technology using Convolutional Neural Networks (CNNs) and cloud computing can be used in the healthcare industry. It provides a new framework that uses CNN to improve the accuracy and speed of ultrasound image processing. It solves the problems of scarce computational resources and inaccessible data at the point of care by using cloud computing to perform image processing, storage, and remote consultation in real-time. Benefits such as automatic image interpretation, consistent data archiving, and remote expert cooperation are just a few of the many highlighted by the research that highlights the transformational potential of this approach to POCUS. The mechanics of putting it all together, how data will be protected, and what this means in the real world for doctors and nurses. It predicts that in the future, with the help of CNNs and cloud computing, POCUS will be easier to use, more accurate, and more collaborative, therefore leading to better treatment and health outcomes for patients.