Open Data Sharing in Clinical Research and Participants Privacy: Challenges and Opportunities in the Era of Artificial Intelligence
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arXiv
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| Format: | Preprint |
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2025
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| author | Hallaj, Shahin Heinke, Anna Kalaw, Fritz Gerald P. Gim, Nayoon Blazes, Marian Owen, Julia Dysinger, Eamon Benton, Erik S. Cordier, Benjamin A. Evans, Nicholas G. Li-Pook-Than, Jennifer Snyder, Michael P. Nebeker, Camille Zangwill, Linda M. Baxter, Sally L. McWeeney, Shannon Lee, Cecilia S. Lee, Aaron Y. Patel, Bhavesh |
| author_facet | Hallaj, Shahin Heinke, Anna Kalaw, Fritz Gerald P. Gim, Nayoon Blazes, Marian Owen, Julia Dysinger, Eamon Benton, Erik S. Cordier, Benjamin A. Evans, Nicholas G. Li-Pook-Than, Jennifer Snyder, Michael P. Nebeker, Camille Zangwill, Linda M. Baxter, Sally L. McWeeney, Shannon Lee, Cecilia S. Lee, Aaron Y. Patel, Bhavesh |
| contents | 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 heightened 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 blur 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 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_01140 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Open Data Sharing in Clinical Research and Participants Privacy: Challenges and Opportunities in the Era of Artificial Intelligence Hallaj, Shahin Heinke, Anna Kalaw, Fritz Gerald P. Gim, Nayoon Blazes, Marian Owen, Julia Dysinger, Eamon Benton, Erik S. Cordier, Benjamin A. Evans, Nicholas G. Li-Pook-Than, Jennifer Snyder, Michael P. Nebeker, Camille Zangwill, Linda M. Baxter, Sally L. McWeeney, Shannon Lee, Cecilia S. Lee, Aaron Y. Patel, Bhavesh Other Quantitative Biology 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 heightened 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 blur 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 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. |
| title | Open Data Sharing in Clinical Research and Participants Privacy: Challenges and Opportunities in the Era of Artificial Intelligence |
| topic | Other Quantitative Biology |
| url | https://arxiv.org/abs/2508.01140 |