ArticleNature medicine2022
A digital mask to safeguard patient privacy.
Article in Nature medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.
What it found
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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.
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Who cites it
25 citing papers in PubMed.
- LLM research on public biosignals data is needed to protect patients.NPJ digital medicine · 2026Review
- Virtual medicine: medical AI in human health and diseases.Military Medical Research · 2026Review
- A lung CT vision foundation model facilitating disease diagnosis and medical imaging.Nature communications · 2025Article
- ROFI: a deep learning-based ophthalmic sign-preserving and reversible patient face anonymizer.NPJ digital medicine · 2025Article
- Current status and solutions for AI ethics in ophthalmology: a bibliometric analysis.NPJ digital medicine · 2025Article
- Increasing pathogenic germline variant diagnosis rates in precision medicine: current best practices and future opportunities.Human genomics · 2025Review
- Developing an artificial intelligence-based progressive growing GAN for high-quality facial profile generation and evaluation through turing test and aesthetic analysis.Scientific reports · 2025Article
- Artificial Intelligence-Enabled Facial Privacy Protection for Ocular Diagnosis: Development and Validation Study.Journal of medical Internet research · 2025Article
- 3D facial imaging: a novel approach for metabolic abnormalities risk profiling.Science China. Life sciences · 2025Article
- Publishing Identifiable Patient Photographs in the Digital Age: Focus Group Study of Patients, Doctors, and Medical Students.Journal of medical Internet research · 2025Article
- CDKD-w+: A Keyframe Recognition Method for Coronary Digital Subtraction Angiography Video Sequence Based on w+ Space Encoding.Sensors (Basel, Switzerland) · 2025Article
- Precision medicine in AML: overcoming resistance.International journal of hematology · 2024Review
- A glimpse into the future: Integrating artificial intelligence for precision HER2-positive breast cancer management.iMetaOmics · 2024Review
- A Practical Guide to Manual and Semi-Automated Neurosurgical Brain Lesion Segmentation.NeuroSci · 2024Article
- Global research of artificial intelligence in eyelid diseases: A bibliometric analysis.Heliyon · 2024Article
- Coordinate-wise monotonic transformations enable privacy-preserving age estimation with 3D face point cloud.Science China. Life sciences · 2024Article
- A narrative review of deep learning in thyroid imaging: current progress and future prospects.Quantitative imaging in medicine and surgery · 2024Review
- Metaverse Wearables for Immersive Digital Healthcare: A Review.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2023Review
- Concerns about using a digital mask to safeguard patient privacy.Nature medicine · 2023Article
- Reply to: Concerns about using a digital mask to safeguard patient privacy.Nature medicine · 2023Article
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Authors and funding
38 authors.
Funding
Abstract
The storage of facial images in medical records poses privacy risks due to the sensitive nature of the personal biometric information that can be extracted from such images. To minimize these risks, we developed a new technology, called the digital mask (DM), which is based on three-dimensional reconstruction and deep-learning algorithms to irreversibly erase identifiable features, while retaining disease-relevant features needed for diagnosis. In a prospective clinical study to evaluate the technology for diagnosis of ocular conditions, we found very high diagnostic consistency between the use of original and reconstructed facial videos (κ ≥ 0.845 for strabismus, ptosis and nystagmus, and κ = 0.801 for thyroid-associated orbitopathy) and comparable diagnostic accuracy (P ≥ 0.131 for all ocular conditions tested) was observed. Identity removal validation using multiple-choice questions showed that compared to image cropping, the DM could much more effectively remove identity attributes from facial images. We further confirmed the ability of the DM to evade recognition systems using artificial intelligence-powered re-identification algorithms. Moreover, use of the DM increased the willingness of patients with ocular conditions to provide their facial images as health information during medical treatment. These results indicate the potential of the DM algorithm to protect the privacy of patients' facial images in an era of rapid adoption of digital health technologies.
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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.