Consideration of Cybersecurity Risks in the Benefit-Risk Analysis of Medical Devices: Scoping Review
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,053 | 0,272 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,002 |
| Méta-épidémiologie (sens large) | 0,007 | 0,010 |
| Bibliométrie | 0,025 | 0,017 |
| Études des sciences et des technologies | 0,002 | 0,003 |
| Communication savante | 0,008 | 0,008 |
| Science ouverte | 0,003 | 0,004 |
| Intégrité de la recherche | 0,006 | 0,004 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,006 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».