Environment scan of generative AI infrastructure for clinical and translational science.

Npj health systems Pub Date : 2025-01-01 Epub Date: 2025-01-25 DOI:10.1038/s44401-024-00009-w

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

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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.

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