How to Prepare for, Survive, and Recover From a Cybersecurity Attack: A Guide for Radiology Practices— <i>AJR</i> Expert Panel Narrative Review
Bibliographic record
Abstract
In an era of persistent and evolving cyberthreats that pose serious risks to patient safety, institutional integrity, and regulatory compliance, health care organizations, particularly radiology departments, must adopt a proactive stance toward cybersecurity. Radiology departments are particularly vulnerable to cyberattacks due to their dependence on digital imaging systems that often are legacy systems and insecure as well as their reliance on network connectivity and specialized software. This AJR Expert Panel Narrative Review offers a strategic road map for health care institutions to prepare for and survive cybersecurity attacks, with a focus on the unique vulnerabilities within medical imaging systems that radiology departments must address. Real-world threats, ranging from PACS network exploitation to DICOM data manipulation, ransomware disruptions, and the consequences of inaction, are examined. Emphasis is placed on practical defense mechanisms, including layered security architecture, regular vulnerability assessments, employee training, and incident response simulations. The insights are intended to inform a defense-in-depth strategy incorporating physical, technical, and administrative safeguards aligned with HIPAA and other regulatory standards. Overall, this guide for radiology practices seeks to align technical controls with operational resilience, to aid practices in detecting, containing, and recovering from cyber incidents with minimal disruption to patient care.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 0.005 |
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 source (direct Gemma or distilled Codex), 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".