Data-driven and artificial intelligence (AI) approach for modelling and analyzing healthcare security practice: a systematic review

Authors

  • Anil Kumar Yadav Yanamala Network Architect Consultant, State of South Carolina department of revenue, 300 A Outlet Pointe Blvd, Columbia, SC 29210 Author

Keywords:

healthcare security, artificial intelligence, data-driven approaches, systematic review, anomaly detection, predictive analytics

Abstract

Healthcare security practices are increasingly adopting data-driven and artificial
intelligence (AI) approaches to enhance their effectiveness and responsiveness in safeguarding
sensitive medical information. This systematic review explores the current landscape of research
and practices in utilizing AI for modeling and analyzing healthcare security. By synthesizing
findings from peer-reviewed literature and industry reports, this review identifies key trends,
methodologies, and challenges in AI-driven healthcare security. The review highlights
advancements in anomaly detection, predictive analytics, and risk assessment models tailored to
healthcare contexts. Furthermore, it discusses the implications of AI adoption on data privacy,
regulatory compliance, and organizational resilience within healthcare settings. The synthesis of
this systematic review provides insights into future research directions and practical implications
for improving healthcare cybersecurity frameworks. 

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Published

2023-10-03

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