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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, 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

Details

Journal npj health systems
Year 2025

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.