Retinal imaging in an era of open science and privacy protection

Abstract

Artificial intelligence (AI) holds great promise for analyzing complex data to advance patient care and disease research. For example, AI interpretation of retinal imaging may enable the development of noninvasive retinal biomarkers of systemic disease. One potential limitation, however, is government regulation regarding retinal imaging as biometric data, which has been recently under debate in the United States. Although careful regard for patient privacy is key to maintaining trust in the widespread use of AI in healthcare, the designation of retinal imaging as biometric data would greatly restrict retinal biomarker research. There are several reasons why retinal imaging should not be considered biometric data. Unlike images of the iris, high quality images of the retina are more difficult to obtain, requiring specialized training and equipment, and often requiring pupil dilation for optimal quality. In addition, retinal imaging features can vary over time with changes in health status, and retinal images are not currently linked to any large identification databases. While the protection of patient privacy is imperative, there is also a need for large retinal imaging datasets to advance AI research. Given the limitations of retinal imaging as a source of biometric data, the research community should work to advocate for the continued use of retinal imaging in AI research.