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Environment scan of generative AI infrastructure for clinical and translational science

Idnay, Betina
Xu, Zihan
Adams, William G
Adibuzzaman, Mohammad
Anderson, Nicholas R
Bahroos, Neil
Bell, Douglas S
Bumgardner, Cody
Campion, Thomas
Castro, Mario
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Authors
Idnay, Betina
Xu, Zihan
Adams, William G
Adibuzzaman, Mohammad
Anderson, Nicholas R
Bahroos, Neil
Bell, Douglas S
Bumgardner, Cody
Campion, Thomas
Castro, Mario
Cimino, James J
Cohen, I Glenn
Dorr, David
Elkin, Peter L
Fan, Jungwei W
Ferris, Todd
Foran, David J
Hanauer, David
Hogarth, Mike
Huang, Kun
Kalpathy-Cramer, Jayashree
Kandpal, Manoj
Karnik, Niranjan S
Katoch, Avnish
Lai, Albert M
Lambert, Christophe G
Li, Lang
Lindsell, Christopher
Liu, Jinze
Lu, Zhiyong
Luo, Yuan
McGarvey, Peter
Mendonca, Eneida A
Mirhaji, Parsa
Murphy, Shawn
Osborne, John D
Paschalidis, Ioannis C
Harris, Paul A
Prior, Fred
Shaheen, Nicholas J
Shara, Nawar
Sim, Ida
Tachinardi, Umberto
Waitman, Lemuel R
Wright, Rosalind J
Zai, Adrian H
Zheng, Kai
Lee, Sandra Soo-Jin
Malin, Bradley A
Natarajan, Karthik
Price Ii, W Nicholson
Zhang, Rui
Zhang, Yiye
Xu, Hua
Bian, Jiang
Weng, Chunhua
Peng, Yifan
Student Authors
Faculty Advisor
Academic Program
Document Type
Journal Article
Publication Date
2025-01-25
Subject Area
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Abstract

This study reports a comprehensive environmental scan of the generative AI (GenAI) infrastructure in the national network for clinical and translational science across 36 institutions supported by the CTSA Program led by the National Center for Advancing Translational Sciences (NCATS) of the National Institutes of Health (NIH) at the United States. Key findings indicate a diverse range of institutional strategies, with most organizations in the experimental phase of GenAI deployment. The results underscore the need for a more coordinated approach to GenAI governance, emphasizing collaboration among senior leaders, clinicians, information technology staff, and researchers. Our analysis reveals that 53% of institutions identified data security as a primary concern, followed by lack of clinician trust (50%) and AI bias (44%), which must be addressed to ensure the ethical and effective implementation of GenAI technologies.

Source

Idnay B, Xu Z, Adams WG, Adibuzzaman M, Anderson NR, Bahroos N, Bell DS, Bumgardner C, Campion T, Castro M, Cimino JJ, Cohen IG, Dorr D, Elkin PL, Fan JW, Ferris T, Foran DJ, Hanauer D, Hogarth M, Huang K, Kalpathy-Cramer J, Kandpal M, Karnik NS, Katoch A, Lai AM, Lambert CG, Li L, Lindsell C, Liu J, Lu Z, Luo Y, McGarvey P, Mendonca EA, Mirhaji P, Murphy S, Osborne JD, Paschalidis IC, Harris PA, Prior F, Shaheen NJ, Shara N, Sim I, Tachinardi U, Waitman LR, Wright RJ, Zai AH, Zheng K, Lee SS, Malin BA, Natarajan K, Price Ii WN, Zhang R, Zhang Y, Xu H, Bian J, Weng C, Peng Y. Environment scan of generative AI infrastructure for clinical and translational science. Npj Health Syst. 2025;2(1):4. doi: 10.1038/s44401-024-00009-w. Epub 2025 Jan 25. PMID: 39872195; PMCID: PMC11762411.

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DOI
10.1038/s44401-024-00009-w
PubMed ID
39872195
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Open Access: This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. © The Author(s) 2025Attribution 4.0 International