Consideration of Cybersecurity Risks in the Benefit-Risk Analysis of Medical Devices: Scoping Review
Bibliographic record
Abstract
BACKGROUND: The integration of connected medical devices (MDs) into health care brings benefits but also introduces new, often challenging-to-assess risks related to cybersecurity, which have the potential to harm patients. Current regulations in the European Union and the United States mandate the consideration of these risks in the benefit-risk analysis (BRA) required for MD approval. This important step in the approval process weighs all the defined benefits of a device with its anticipated risks to ensure that the product provides a positive argument for use. However, there is limited guidance on how cybersecurity risks should be systematically evaluated and incorporated into the BRA. OBJECTIVE: This scoping review aimed to identify current legal frameworks, guidelines, and standards in the United States, Canada, South Korea, Singapore, Australia, the United Kingdom, and the European Union on how cybersecurity risks should be considered in the BRA of MDs. METHODS: This scoping review followed the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) framework. A systematic literature search of 10 databases was conducted in two phases on July 3, 2024 and September 30, 2024, including the guidance databases of the Food and Drug Administration, the Medical Device Coordination Group, and other International Medical Device Regulators Forum members; the International Medical Device Regulators Forum database; PubMed; and Scopus. Search terms included "cybersecurity," "security," "benefit/risk," "benefit-risk," and "risk-benefit." Additional references were identified via citation searching and expert interviews. Inclusion criteria were met if a document was a guideline or standard in force that provided guidance on the BRA or cybersecurity risks of MDs. Documents were excluded when they were not relevant to MDs, they were limited to a subclass of devices, they were about in vitro diagnostic MDs or investigational devices, and the content of the source was insufficient to undertake a scientific analysis. Data were extracted and analyzed using MAXQDA 2022, and the findings were narratively summarized and visualized in figures and tables. RESULTS: The search identified 150 documents, with 34 (22.7%) meeting the inclusion criteria. These 34 documents included 4 (12%) regulations, 5 (15%) standards, 6 (18%) technical reports, and 19 (56%) guidance documents. While cybersecurity risks were acknowledged in most documents, detailed methods for their integration into the BRA were lacking. Some standards and guidelines provided examples of how to consider cybersecurity risks in the BRA, but a comprehensive and standardized approach was lacking. CONCLUSIONS: This review highlights a substantial gap between the recognition of cybersecurity risks in MDs and the guidance on their incorporation into the BRA. Standardized frameworks are needed to provide clear methods for evaluating cybersecurity risks and their impact on the safety and security of MDs.
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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 | 0.053 | 0.272 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.010 |
| Bibliometrics | 0.025 | 0.017 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".