A Self-Adaptive IoT-based Approach for Improving the Decision Making of Active Surgical Robots in Hospitals
This work addresses decision-making challenges for surgeons and hospitals in employing surgical robots, but it appears incremental as it applies existing MCDA methods in a new IoT-based context.
The paper tackles the problem of improving decision-making for using surgical robots in hospitals by proposing an IoT-based self-adaptive approach using multi-criteria decision analysis (MCDA), with experimental validation showing it enhances decisions in robotic surgical procedures.
In recent years, surgical robots have become instrumental tools for assisting surgeons in performing complex surgical procedures in hospitals. Unlike conventional surgical methods, robotic systems help surgeons, for example, to perform minimally invasive surgical procedures while enhancing the precision and control of operations (e.g. tiny incisions, wound sutures, endoscopic suturing, among others). To this extent, it is essential to consider several factors that may influence the feasibility and decision making of employing robotic systems in surgical procedures. In this paper, we propose an IoT-based self-adaptive approach that uses multi-criteria decision analysis methods (MCDA) for enhancing the decision making of operations involving surgical robots. Throughout this paper, we present experimental validation results in utilizing MCDA as an effective strategy for enhancing the decisions of employing robotic systems in surgical procedures.