A Study on Quantum Neural Networks in Healthcare 5.0

TL;DR

This paper explores the emerging role of quantum neural networks in Healthcare 5.0, analyzing case studies and literature to identify research gaps, challenges, and future directions in integrating quantum AI into healthcare analytics.

Contribution

It provides a comprehensive review of quantum neural networks in healthcare, highlighting research gaps and proposing future research directions for Healthcare 5.0.

Findings

Quantum neural networks are increasingly relevant in Healthcare 5.0.

Current research gaps exist in understanding quantum AI's impact on healthcare.

Challenges include integration and performance evaluation of quantum systems.

Abstract

The working environment in healthcare analytics is transforming with the emergence of healthcare 5.0 and the advancements in quantum neural networks. In addition to analyzing a comprehensive set of case studies, we also review relevant literature from the fields of quantum computing applications and smart healthcare analytics, focusing on the implications of quantum deep neural networks. This study aims to shed light on the existing research gaps regarding the implications of quantum neural networks in healthcare analytics. We argue that the healthcare industry is currently transitioning from automation towards genuine collaboration with quantum networks, which presents new avenues for research and exploration. Specifically, this study focuses on evaluating the performance of Healthcare 5.0, which involves the integration of diverse quantum machine learning and quantum neural network systems. This study also explores a range of potential challenges and future directions for Healthcare 5.0, particularly focusing on the integration of quantum neural networks.