ArticleEClinicalMedicine2024
Re-identification of anonymised MRI head images with publicly available software: investigation of the current risk to patient privacy.
Article in EClinicalMedicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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.
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.
Who cites it
12 citing papers in PubMed.
- Ethical and regulatory aspects of facial scanning in dentistry: a scoping review.Clinical oral investigations · 2026Article
- Data After Death: Post-Mortem Ethics of Burn Patient Records and Images.Journal of burn care & research : official publication of the American Burn Association · 2026Review
- Artificial intelligence for pediatric neuroimaging.Pediatric radiology · 2026Review
- De-Identification of Magnetic Resonance Imaging to Protect Patient Privacy in Research Use: A Comprehensive Review.Healthcare informatics research · 2026Article
- Opportunities and pitfalls of data contextualization in neuroimaging.Nature reviews. Neuroscience · 2026Review
- Automatic Personal Identification Using a Single MRI Slice.Bioengineering (Basel, Switzerland) · 2026Article
- Unlocking Health Data for Research: Legal, Technical, and Organisational Lessons from a Belgian Interdisciplinary Case Study.Journal of healthcare informatics research · 2026Article
- A low frequency damaging SORCS2 variant identified in a family with ADHD compromises receptor stability and quenches activity.Molecular psychiatry · 2026Article
- Artificial Intelligence and Novel Trial Designs for Acute Ischemic Stroke: Opportunities and Challenges.Stroke · 2026Review
- Large Language Models in Radiologist-Patient Communication: A Narrative Review for Clinical Practice.Cureus · 2026Review
- ROFI: a deep learning-based ophthalmic sign-preserving and reversible patient face anonymizer.NPJ digital medicine · 2025Article
- Measuring the potential risk of re-identification of imaging research participants from open-source automated face recognition software.NeuroImage · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Facial recognition software (FRS) has historically been perceived as lacking the capability to identify individuals from cross-sectional medical images. Utilising such data for identification purposes was considered infeasible due to the substantial computational power and specialised technical expertise it would require. However, recent advancements in accessible artificial intelligence-based (AI-based) software and open-source tools have made these applications widely available and easy to use, raising new privacy concerns. Methods: This proof-of-concept was designed as a cross-sectional study and included participants with a verified online presence. Standard magnetic resonance imaging (MRI) head scans were performed on these participants, from which three-dimensional rendering (3DR) images were created using free and publicly available software. These images were used for face searches by free and publicly available FRS. Different head orientations and hairstyles were applied to the 3DR images to assess whether non-facial features influenced the FRS results. All results were obtained between the 10th of February 2024 and the 1st of March 2024. Findings: Face searches of 3DR images in a database containing over 800 million images from the World Wide Web (WWW) yielded correct matches for 50% of the participants in less than 10 min. The user-friendly software required minimal computational knowledge or resources, making this process broadly accessible. Modifying elements such as hairstyles or the orientation of the 3DR to better resemble actual photographs of the participants improved FRS matches. Interpretation: Current existing FRS can swiftly and accurately identify individuals from MRI head scans. This poses a significant privacy risk for participants in enrolled clinical trials and highlights the urgent need for improved data protection measures and increased sensitivity to ensure participant confidentiality. Funding: There was no funding source for this study.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.