An Approach of Sandbox Technology for Improving the Security in Online Healthcare Systems

Abstract

Medical Surgical procedures, especially those in neurology, are high risk stakes, intricate situations that require a significant mental investment from surgical teams. Despite being intent the security  is the serious problem in the online Healthcare systems. Although practice and education can improve cognitive abilities, there are still few opportunities for surgical training because of patient safety concerns. We propose medical SurgBox, an agent-driven sandbox framework designed to methodically improve surgeons' cognitive abilities in realistic surgical simulations in order to address these cognitive difficulties in surgical training and practice. Our SurgBox specifically uses Multi Large Language Models(MLMs) with customised Retrieval-Augmented Generation (RAG) to simulate a variety of surgical jobs in an authentic manner, providing realistic training settings for purposeful practice. To reduce the cognitive strain on surgical teams during surgery, we specifically developed Surgery Copilot, an AI-driven assistant that actively coordinates the surgical information stream and aids in clinical decision making. Through the use of a unique Long-Short Memory mechanism, our Surgery Copilot is able to successfully strike a balance between providing prompt procedural help and having extensive surgical expertise. Our Med SurgBox framework's ability to improve surgical cognitive capacities and assist clinical decision-making has been validated through extensive tests utilising actual neurosurgical procedure records. Our SurgBox architecture improves surgical education and practice by offering an integrated training and operational support solution to solve cognitive obstacles, which has the potential to revolutionise surgical results and healthcare quality.