DefinePK

Abstract

The integration of medical devices and applications with internet-connected technologies,known as Internet of Medical Things (IoMT), has enabled remote monitoring and management of patienthealth. In socially implemented IoMT systems, such as mobile health applications or wearable devices,analyzing the sentiment expressed in patient feedback or reviews is crucial to ensure patient satisfactionand improve the quality of care. However, such sentiment analysis can be easily manipulated byintentional attacks or adversarial inputs, such as fake reviews or manipulated feedback. This paperpresents a review of the efficacy of adversarial learning-based sentiment analysis techniques in improvingsentiment analysis (SA) in socially implemented IoMT systems. This paper discusses the challenges ofsentiment analysis in IoMT systems and how adversarial learning can be applied to improve therobustness of sentiment analysis models. This study highlights the potential of adversarial learningtechniques to improve the accuracy and effectiveness of sentiment analysis in socially implemented IoMTsystems. However, the paper also shows that more research is needed to fully understand the impact ofadversarial inputs on sentiment analysis models and to develop more sophisticated and robust models.This paper concludes by discussing future directions for research in this domain, including the need forbetter data privacy and security measures to prevent adversarial attacks in IoMT systems.