ReviewMayo Clinic proceedings. Digital health2023
Retinal Scans and Data Sharing: The Privacy and Scientific Development Equilibrium.
Review in Mayo Clinic proceedings. Digital health, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 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
9 citing papers in PubMed.
- Evaluating the prevalence and application of data sharing statements in high-impact ophthalmology journals.Eye (London, England) · 2026Article
- Federated learning for privacy-preserving ophthalmic artificial intelligence: clinical applications and translational challenges.Frontiers in medicine · 2026Review
- Navigating open data sharing and privacy in the age of clinical AI research: from reidentification to pseudo-reidentification.EClinicalMedicine · 2026Review
- Article
- Highly efficient homomorphic encryption-based federated learning for diabetic retinopathy classification.Journal of medical imaging (Bellingham, Wash.) · 2025Article
- Artificial intelligence, data sharing, and privacy for retinal imaging under Brazilian Data Protection Law.International journal of retina and vitreous · 2025Article
- Smartphone conjunctiva photography for malaria risk stratification in asymptomatic school age children.NPJ digital medicine · 2025Article
- Artificial intelligence in ophthalmology: opportunities, challenges, and ethical considerations.Medical hypothesis, discovery & innovation ophthalmology journal · 2025Review
- Moving forward with the European health data space: the need to restore trust in European health systems.The Lancet regional health. Europe · 2024Article
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
In ophthalmology, extensive use of ancillary imaging has enabled the development of artificial intelligence models, for which data are crucial. A data-sharing environment promotes external validation, collaborative research, and bias assessment before implementation in the real world; however, legal and ethical concerns need to be addressed in this process. The proposed solutions for improving the security of ophthalmic data sharing are patient consent and data-sharing agreements with third parties. Federated learning enables decentralized algorithm development, however, with limited results and unknown risks. Deidentification techniques through image manipulations and synthetically generated images are possible alternatives to improve security. Still, there is no single solution available. The challenge is to determine the appropriate level of risk and ensure accountability for the use of data. Sharing data, including retinal scans, can and should be performed within a trusted research environment, where there are data use agreements and credentialing of researchers, including requirements for training in responsible conduct of data use. In this review, we discuss the challenges and consequences surrounding limited sharing of ophthalmic datasets in the development of digital innovations and explore potential solutions that will enable safer sharing of retinal scan data.
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.