A systematic review of privacy-preserving methods deployed with blockchain and federated learning for the telemedicine

Madhuri Hiwale; Rahee Walambe; Vidyasagar Potdar; Ketan Kotecha

doi:10.1016/j.health.2023.100192

A systematic review of privacy-preserving methods deployed with blockchain and federated learning for the telemedicine

Healthc Anal (N Y). 2023 Nov:3:100192. doi: 10.1016/j.health.2023.100192. Epub 2023 May 5.

Authors

Madhuri Hiwale¹, Rahee Walambe^{1

2}, Vidyasagar Potdar³, Ketan Kotecha^{1

2}

Affiliations

¹ Symbiosis Institute of Technology, Symbiosis International (Deemed University), Pune 412115, India.
² Symbiosis Centre for Applied Artificial Intelligence (SCAAI), Symbiosis International (Deemed University), Pune 412115, India.
³ Blockchain R&D Lab, School of Management and Marketing, Curtin University, Perth 6107, Australia.

Abstract

The unexpected and rapid spread of the COVID-19 pandemic has amplified the acceptance of remote healthcare systems such as telemedicine. Telemedicine effectively provides remote communication, better treatment recommendation, and personalized treatment on demand. It has emerged as the possible future of medicine. From a privacy perspective, secure storage, preservation, and controlled access to health data with consent are the main challenges to the effective deployment of telemedicine. It is paramount to fully overcome these challenges to integrate the telemedicine system into healthcare. In this regard, emerging technologies such as blockchain and federated learning have enormous potential to strengthen the telemedicine system. These technologies help enhance the overall healthcare standard when applied in an integrated way. The primary aim of this study is to perform a systematic literature review of previous research on privacy-preserving methods deployed with blockchain and federated learning for telemedicine. This study provides an in-depth qualitative analysis of relevant studies based on the architecture, privacy mechanisms, and machine learning methods used for data storage, access, and analytics. The survey allows the integration of blockchain and federated learning technologies with suitable privacy techniques to design a secure, trustworthy, and accurate telemedicine model with a privacy guarantee.

Keywords: Blockchain; Federated learning; Healthcare; Privacy-preserving methods; Telemedicine.

Publication types

Review