ReviewCureus2025
Evolving Zero Trust Architectures for AI-Driven Cyber Threats in Healthcare and Other High-Risk Data Environments: A Systematic Review.
Review in Cureus, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed.
- Ransomware attacks and cybersecurity concerns in modern hospitals: vulnerabilities and impacts on trauma centers and patient care.Trauma surgery & acute care open · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The rapid adoption of artificial intelligence (AI) in healthcare and other high-risk environments has introduced sophisticated cyber threats that challenge traditional security models. Zero Trust Architecture (ZTA), with its principle of "never trust, always verify," has emerged as a promising framework to counter these evolving risks. This systematic review examines the current state of ZTA implementations in mitigating AI-driven cyber threats, focusing on healthcare systems, and identifies gaps between theoretical principles and real-world applications. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 guidelines, we conducted a comprehensive search across five databases (IEEE Xplore, PubMed, Scopus, Web of Science, and ACM Digital Library), identifying 299 records. After removing duplicates and screening for relevance, 15 studies met the inclusion criteria. These studies were analyzed for themes related to ZTA components, AI threat mitigation, implementation challenges, and ethical considerations. The Mixed Methods Appraisal Tool (MMAT) was used to assess methodological quality and risk of bias. The review revealed that while ZTA principles are well-suited to address AI-driven threats, particularly through explainable AI (XAI) and continuous monitoring, significant gaps persist in standardization, empirical validation, and stakeholder trust. Key findings include (1) a lack of metrics to evaluate ZTA efficacy against adversarial AI; (2) ethical and regulatory hurdles, such as algorithmic bias and data privacy concerns; and (3) operational barriers like interoperability issues and clinician resistance. Only four of the 15 studies provided real-world evidence of ZTA implementations, highlighting a critical research-practice divide. ZTA represents a transformative approach to cybersecurity in AI-augmented environments, but its potential remains underutilized due to theoretical dominance and implementation challenges. Future efforts must prioritize interdisciplinary collaboration, standardized frameworks, and pilot studies to bridge these gaps. Without actionable advancements, ZTA risks being outpaced by the very AI threats it seeks to mitigate. This review underscores the urgent need for adaptive, evidence-based ZTA models tailored to high-risk sectors, such as healthcare.
Indexed as
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.