AI-DRIVEN CYBERSECURITY FOR TELEHEALTH PLATFORMS: SAFEGUARDING REMOTE HEALTHCARE

This article explores the integration of artificial intelligence (AI) in enhancing cybersecurity for telehealth platforms. It examines the rapid growth of telehealth adoption, the associated security challenges, and how AI-powered solutions can address vulnerabilities in this domain. The article discusses key AI applications in telehealth security, including anomaly detection, real-time threat analysis, automated response systems, enhanced authentication, and natural language processing for policy compliance. Additionally, it highlights critical challenges in implementing AI for telehealth security, such as data privacy concerns, the need for explainable AI, managing false positives, combating adversarial attacks, and ensuring regulatory compliance. By presenting both the potential and limitations of AI in this context, the article provides a comprehensive overview of the evolving landscape of AI-driven cybersecurity in telehealth.

Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2024-10-21
DOI
https://doi.org/10.5281/zenodo.13960151
Primary Topic
Impact of AI and Big Data on Business and Society
Type
article
Field-Weighted Citation Impact
0.00
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article

AI-DRIVEN CYBERSECURITY FOR TELEHEALTH PLATFORMS: SAFEGUARDING REMOTE HEALTHCARE

Zenodo (CERN European Organization for Nuclear Research)
Impact of AI and Big Data on Business and Society
article

AI-DRIVEN CYBERSECURITY FOR TELEHEALTH PLATFORMS: SAFEGUARDING REMOTE HEALTHCARE

article en

Abstract

This article explores the integration of artificial intelligence (AI) in enhancing cybersecurity for telehealth platforms. It examines the rapid growth of telehealth adoption, the associated security challenges, and how AI-powered solutions can address vulnerabilities in this domain. The article discusses key AI applications in telehealth security, including anomaly detection, real-time threat analysis, automated response systems, enhanced authentication, and natural language processing for policy compliance. Additionally, it highlights critical challenges in implementing AI for telehealth security, such as data privacy concerns, the need for explainable AI, managing false positives, combating adversarial attacks, and ensuring regulatory compliance. By presenting both the potential and limitations of AI in this context, the article provides a comprehensive overview of the evolving landscape of AI-driven cybersecurity in telehealth.

Zenodo (CERN European Organization for Nuclear Research)
Peace, Justice and strong institutions
Openalex Percentile: Top 48%
Impact of AI and Big Data on Business and Society
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