Retinal Scans and Data Sharing: The Privacy and Scientific Development Equilibrium

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.