Artificial Intelligence for Strengthening Cybersecurity in U.S. Healthcare Systems
DOI:
https://doi.org/10.54536/ajmsi.v4i2.6178Keywords:
Artificial Intelligence, Cybersecurity, Data Protection, Healthcare, Threat Detection, U.S. SystemsAbstract
The US healthcare industry is grappling with a new level of cybersecurity threats. In fact, data breaches reached 275 million people just in 2024, which is equal to 82% of the U.S. population. All the old ways of doing cybersecurity remain insufficient in the face of ever-evolving threats. These traditional methods involve signature and perimeter-based detection techniques, as well as rule-based access policies. Even with robust cybersecurity investment, 92% of healthcare providers were hit by data breaches in the past few years. This paper explores the use of artificial intelligence technologies to improve cybersecurity in US healthcare systems. This study uses a systematic literature review approach. The research examines today’s threat landscapes, traditional security shortcomings and AI-driven approaches. The study synthesizes data from various sources, including recent studies published in the academic literature, online databases and industry reports covering 2019-2025. The results showed that AI-based methods outperform the traditional techniques. These methods include machine learning based anomaly detection, deep learning models for zero-day exploit detection, and natural language processing (NLP)-based threat analysis. However, implementation challenges persist. These difficulties are the adversarial attacks on AI systems, the transparency of the algorithm, false positives, and HIPAA compliance. The study concludes that artificial intelligence offers transformative potential for healthcare cybersecurity; thus, successful deployment requires careful integration with existing infrastructure, continuous model updating, and collaboration between AI systems and human security specialists.
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Copyright (c) 2025 Barbara Aryeley Aryee, Jehoiarib Umoren, Kwadwo Adu Agymang
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