Evidence map›Paper›PMID 37945583›Full record

ArticleScientific reports2023

Cybersecurity vulnerability analysis of medical devices purchased by national health services.

Lorenzo Bracciale, Pierpaolo Loreti, Giuseppe Bianchi

Abstract read
In one paragraph

Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

7 citing papers in PubMed.

  1. Article
  2. Light-Induced Entropy for Secure Vision.Advanced materials (Deerfield Beach, Fla.) · 2026
    Article
  3. Review
  4. Article
  5. Article
  6. Article
  7. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Lorenzo BraccialeDepartment of Electronic Engineering, University of Rome Tor Vergata, Rome, Italy. lorenzo.bracciale@uniroma2.it.
Pierpaolo LoretiDepartment of Electronic Engineering, University of Rome Tor Vergata, Rome, Italy.
Giuseppe BianchiDepartment of Electronic Engineering, University of Rome Tor Vergata, Rome, Italy.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The growing integration of software within medical devices introduces the potential for cybersecurity threats. How significant is this risk, and to what extent are citizens currently exposed? In this study, we adopt a new data-gathering methodology using datasets provided in Open Contracting Data Standard (OCDS). This allowed us to perform an extensive analysis across over 36 countries within a 12-year range, searching 92 million public administration purchase records for potentially vulnerable medical devices. The findings reveal a concerning landscape wherein numerous medical devices purchased by national health services possessed or still possess 661 distinct vulnerabilities-more than half of which are deemed critical or high-severity. These vulnerabilities enable relatively simple attacks to impact data confidentiality, integrity, and accessibility severely. Even if patches were applied immediately upon discovery, these vulnerabilities would still result in roughly 3.2 years of system exposure from the time a device is purchased until a software vulnerability is announced, with all classes of devices affected, including high-risk IIB and III devices which accounts for 74% of instances. While a full analysis requires interactivity, this noninvasive methodology enables a large-scale study, emphasizing the need to move faster from the safety to the security of medical devices.

Indexed as

SoftwareState MedicineComputer SecurityConfidentiality

Identifiers

PMID37945583
PMCPMC10636100

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.