In this commentary in the journal Translational Vision Science and Technology, Dr. Elysse Tom and her co-authors discuss digital data privacy in the era of "Big Data" and artificial intelligence. Although these new technologies offer many potential benefits for patients and for healthcare systems, there is a need for better data protection methods that can evolve with these advances.
The authors discuss some of the ethical issues associated with the use of these increasingly large clinical datasets, noting that maintaining data privacy and confidentiality-and thus respect for persons-is a challenge. For example, breaches of patient privacy can cause major harms and can also have unintended consequences, such as impacting employment or insurance coverage or allowing hackers to obtain Social Security numbers and personal financial information.
Data sharing is another problem, due to risk of data breaches and even malicious privacy violations. The issue of patient consent can be problematic as well - many patients are supportive of the use of their data to improve health care and research but feel that they should be asked to give permission first, which may not be feasible for large datasets of routinely collected data.
The authors explore some of the other issues associated with data privacy as well as some of the proposed ways to address them, including regulatory approaches. Some potential strategies involve using technology to combat this problem by harnessing machine learning techniques such as federated learning (or collaborative learning) to protect data by training algorithms across multiple servers using separate data samples. The authors conclude that a successful balance between privacy and technology is altogether possible if we can develop thoughtful solutions that are capable of adapting to the evolving technology.
Tom E, Keane PA, Blazes M, Pasquale LR, Chiang MF, Lee AY, Lee CS, and AAO Artificial Intelligence Task Force. Protecting Data Privacy in the Age of AI-Enabled Ophthalmology. Trans. Vis. Sci. Tech. July 2020, Vol.9, 36. doi:https://doi.org/10.1167/tvst.9.2.36