Artificial Intelligence & Blockchain Technology in Modern Telehealth Systems-A Systematic Review

Background: Telehealth systems have significantly transformed healthcare delivery, particularly in remote areas. However, traditional telehealth models face substantial security and privacy challenges, which hinder their effectiveness and safety. Aim: This study aims to explore the potential of Artificial Intelligence (AI) and blockchain technology in addressing the security vulnerabilities and privacy concerns in telehealth systems and to propose an integrated solution that enhances the efficiency, security, and scalability of these systems. Methodology: The Study presents a survey on existing research on telehealth, and challenges, evaluating AI's role in improving healthcare delivery through data analytics and blockchain's ability to secure healthcare data. The study introduces the Smart Secure Telehealth (SST) algorithm, combining AI and blockchain in an eight-step telehealth operation model covering system initialization, patient data handling, and AI-powered analysis. Result: The paper presents a robust, secure, and scalable telehealth framework. The study highlights the necessity of integrating AI and blockchain for a more secure, patient-centered telehealth system, with potential future improvements including IoT integration for enhanced health monitoring. In addition, the survey presents important gaps for further studies. The proposed Smart Secure Telehealth (SST) algorithm presents a robust solution that may enhances the security, efficiency, and scalability of telehealth systems, paving the way for a more secure and patient-centered healthcare future, if carefully implemented.

Authors

Publication Details

Journal
KASU Journal of Computer Science
Published
2024-09-30
DOI
https://doi.org/10.47514/kjcs/2024.1.3.0015
Citations
1
Primary Topic
COVID-19 diagnosis using AI
Type
article
Field-Weighted Citation Impact
0.40
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article

Artificial Intelligence & Blockchain Technology in Modern Telehealth Systems-A Systematic Review

Daniel D. Wisdom, Samson Isaac, Odunayo Esther Oduntan, Hassan J. Bature et al.
1 citations
KASU Journal of Computer Science
COVID-19 diagnosis using AI
0.40
article

Artificial Intelligence & Blockchain Technology in Modern Telehealth Systems-A Systematic Review

Daniel D. Wisdom, Samson Isaac, Odunayo Esther Oduntan, Hassan J. Bature, Ugwunna Charles Okechukwu, Cathrine Febisola Falayi
article en
1 citations

Abstract

Background: Telehealth systems have significantly transformed healthcare delivery, particularly in remote areas. However, traditional telehealth models face substantial security and privacy challenges, which hinder their effectiveness and safety. Aim: This study aims to explore the potential of Artificial Intelligence (AI) and blockchain technology in addressing the security vulnerabilities and privacy concerns in telehealth systems and to propose an integrated solution that enhances the efficiency, security, and scalability of these systems. Methodology: The Study presents a survey on existing research on telehealth, and challenges, evaluating AI's role in improving healthcare delivery through data analytics and blockchain's ability to secure healthcare data. The study introduces the Smart Secure Telehealth (SST) algorithm, combining AI and blockchain in an eight-step telehealth operation model covering system initialization, patient data handling, and AI-powered analysis. Result: The paper presents a robust, secure, and scalable telehealth framework. The study highlights the necessity of integrating AI and blockchain for a more secure, patient-centered telehealth system, with potential future improvements including IoT integration for enhanced health monitoring. In addition, the survey presents important gaps for further studies. The proposed Smart Secure Telehealth (SST) algorithm presents a robust solution that may enhances the security, efficiency, and scalability of telehealth systems, paving the way for a more secure and patient-centered healthcare future, if carefully implemented.

KASU Journal of Computer ScienceVol. 1(3)
Openalex Percentile: Top 34%
COVID-19 diagnosis using AI
0.40
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