Navigating open data sharing and privacy in the age of clinical AI research: from reidentification to pseudo-reidentification

TL;DR

This paper discusses how AI challenges data privacy in clinical research and introduces a new approach to open data sharing through the AI-READI project.

Contribution

The paper introduces the concept of pseudo-reidentification and proposes a novel open data sharing approach for clinical AI research.

Findings

AI methods are increasing the risk of reidentifying de-identified clinical data.

The AI-READI project offers a new model for balancing data sharing and privacy in clinical research.

Pseudo-reidentification challenges traditional definitions of identifiable data.

Abstract

Sharing clinical research data is key for increasing the pace of medical discoveries that improve human health. However, concern about study participants' privacy, confidentiality, and safety is a major factor that deters researchers from openly sharing clinical data even after deidentification. This concern is further enhanced by the evolution of artificial intelligence (AI) approaches that pose an ever-increasing threat to the reidentification of study participants. Here, we discuss the challenges AI approaches create that are blurring the lines between identifiable, and non-identifiable data. We present a concept of pseudo-reidentification, and discuss how these challenges provide opportunities for rethinking open data sharing practices in clinical research. We highlight the novel open data sharing approach we have established as part of the AI-READI (Artificial Intelligence Ready, and Exploratory Atlas for Diabetes Insights) project, one of the four Data Generation Projects funded by the National Institutes of Health Common Fund's Bridge2AI Program.

Figures