An Overview on AIIoT Based Decision-Making System in Robotic Surgery: KSK Approach in Robotic Surgery

Abstract

The intersection of Artificial Intelligence (AI) and the Internet of Things (IoT) is ushering in a paradigm shift in surgical robotics, shifting from teleoperated support to autonomous, data-centric decision-making. In this research, we study the symbiotic interaction of robotic surgery with AI-driven IoT systems, where deep learning algorithms evaluate real-time streams of sensor data from IoT-enabled surgical tools to offer intraoperative guidance. With high-speed data analytics, edge computing, and predictive modelling, these technologies give surgeons better situational awareness, automatic risk assessment, and accurate error reduction. We explore implications of convergence of these technologies for real-time surgical instrument monitoring, tissue characterisation and automated procedural workflow optimisation. We also examine the issues of low latency communication, cybersecurity and the ethical implications of machine-guided decision support in high-stakes clinical situations. This synthesis highlights the promise of AI-IoT ecosystems to reduce cognitive strain, eliminate intra-operative variability, and ultimately enhance patient outcomes in complex minimally invasive procedures. The KSK approach is built based on the latest research in AI-driven IoT (AIIoT) and decision-making methods in specialised healthcare monitoring to deliver high-accuracy monitoring. In robotic and endoscope-assisted neurosurgery, the KSK technique offers great precision, with “Gross Total Resection” (GTR) achieved in over 85% of patients. Robot-assisted keyhole techniques are more accurate than freehand approaches, with variations in the entry point sometimes reduced to (< 1 mm).