Zero-Trust Architecture for Smart Hospitals: A Virtual Blueprint for Cyber-resilient Healthcare Infrastructure
Bibliographic record
Abstract
The rapid digital transformation of healthcare through smart hospitals driven by AI, IoMT, cloud computing, and telemedicine has heightened cyber vulnerabilities, with 276 million records breached globally in 2024. This study developed a Zero Trust Architecture (ZTA) blueprint to strengthen cybersecurity in smart hospitals, addressing the challenges of diverse device ecosystems and regulatory compliance. Drawing on a comprehensive literature review, the research established ZTA’s theoretical foundation, emphasizing continuous verification rather than traditional perimeter defenses. The study is broadly applicable and applied a Design Science Research approach and mixed-methods analysis, combining risk models, maturity assessments, and machine learning for IoMT threat detection. Results showed significant improvements: a two-thirds reduction in cyber risks, over 95% accuracy in detecting IoMT threats, strong compliance with HIPAA requirements, and a threefold return on investment. The blueprint proved scalable across different hospital types, though limitations include reliance on simulated datasets. Recommendations highlight the need for tailored IoMT datasets, integration of explainable AI, real-world deployment, standardized metrics through collaboration, and adaptive algorithms for evolving threats. Overall, this research provides a practical and evidence-based framework to enhance the resilience of smart hospitals, safeguard patient safety and ensure operational continuity.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".