Title: A Review of AI-driven Cybersecurity Systems for Protecting Healthcare Systems in the United States.
Authors: Chinonso Valentine Nnachetam
Volume: 10
Issue: 1
Pages: 44-47
Publication Date: 2026/01/28
Abstract:
The increase in the digitization of healthcare systems in the United States, by the use of technologies such as Electronic Health Records (EHRs), Health Information Systems (HIS), and the Internet of Medical Things (IoMT), has drastically enhanced the operations of healthcare delivery while at the same time exposing the healthcare institutions to ever rising cybersecurity threats. Several cyber attacks such as ransomware, phishing, insider threats, and false data injection have become prevalent, bringing about serious risks to patient safety, data privacy, and operational continuity. This paper carried out a critical review of AI-driven cybersecurity systems which have been deployed to protect healthcare systems in the United States. By carrying out a review on existing literature, the study examined common cyber threats that are affecting healthcare environments and analyzed the use of AI and machine learning techniques - including intrusion detection, anomaly detection, clustering, and access control mechanisms - for the detection of threats and their mitigation. The limitations in the existing AI-mechanisms were critically evaluated with respect to scalability, data availability, interpretability, regulatory compliance, and cost of implementation. Findings from this review showed that the existing mitigation frameworks were developed for well-resourced urban healthcare institutions, making them expensive and complex for rural and small healthcare clinics. There is a need to develop a lightweight, interpretable, and cost-effective AI-driven cybersecurity frameworks which can be utilized by rural health clinics.