A Self-Adaptive IoT-based Approach for Improving the Decision Making of Active Surgical Robots in Hospitals

TL;DR

This paper proposes a self-adaptive IoT-based system utilizing multi-criteria decision analysis to improve decision making in the deployment of surgical robots, validated through experimental results.

Contribution

It introduces a novel IoT-enabled self-adaptive approach using MCDA to enhance surgical robot decision making, addressing a gap in adaptive surgical systems.

Findings

MCDA improves decision accuracy for robotic surgery deployment

Experimental validation shows enhanced decision support

System effectively adapts to complex surgical scenarios

Abstract

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.