Semantic Web in Healthcare: A Systematic Literature Review of Application, Research Gap, and Future Research Avenues

TL;DR

This systematic review analyzes how Semantic Web technologies are applied in healthcare, identifies research gaps, and suggests future directions to improve data interoperability, decision support, and e-health systems.

Contribution

It provides a comprehensive review of 65 papers on SW in healthcare, categorizing research themes, and highlighting gaps and future research avenues.

Findings

Identified five key research themes in SW healthcare applications

Highlighted existing research gaps and challenges

Proposed future research directions for SW in healthcare

Abstract

Today, healthcare has become one of the largest and most fast-paced industries due to the rapid development of digital healthcare technologies. The fundamental thing to enhance healthcare services is communicating and linking massive volumes of available healthcare data. However, the key challenge in reaching this ambitious goal is letting the information exchange across heterogeneous sources and methods as well as establishing efficient tools and techniques. Semantic Web (SW) technology can help to tackle these problems. They can enhance knowledge exchange, information management, data interoperability, and decision support in healthcare systems. They can also be utilized to create various e-healthcare systems that aid medical practitioners in making decisions and provide patients with crucial medical information and automated hospital services. This systematic literature review (SLR) on SW in healthcare systems aims to assess and critique previous findings while adhering to appropriate research procedures. We looked at 65 papers and came up with five themes: e-service, disease, information management, frontier technology, and regulatory conditions. In each thematic research area, we presented the contributions of previous literature. We emphasized the topic by responding to five specific research questions. We have finished the SLR study by identifying research gaps and establishing future research goals that will help to minimize the difficulty of adopting SW in healthcare systems and provide new approaches for SW-based medical systems progress.