Environment Scan of Generative AI Infrastructure for Clinical and Translational Science
Creators: name: Idnay, Betina
name: Xu, Zihan
name: Adams, William G.
name: Adibuzzaman, Mohammad
name: Anderson, Nicholas R.
name: Bahroos, Neil
name: Bell, Douglas S.
name: Bumgardner, Cody
name: Campion, Thomas
name: Castro, Mario
name: Cimino, James J.
name: Cohen, I. Glenn
name: Dorr, David
name: Elkin, Peter L
name: Fan, Jungwei W.
name: Ferris, Todd
name: Foran, David J.
name: Hanauer, David
name: Hogarth, Mike
name: Huang, Kun
name: Kalpathy-Cramer, Jayashree
name: Kandpal, Manoj
name: Karnik, Niranjan S.
name: Katoch, Avnish
name: Lai, Albert M.
name: Lambert, Christophe G.
name: Li, Lang
name: Lindsell, Christopher
name: Liu, Jinze
name: Lu, Zhiyong
name: Luo, Yuan
name: McGarvey, Peter
name: Mendonca, Eneida A.
name: Mirhaji, Parsa
name: Murphy, Shawn
name: Osborne, John D.
name: Paschalidis, Ioannis C.
name: Harris, Paul A.
name: Prior, Fred
name: Shaheen, Nicholas J.
name: Shara, Nawar
name: Sim, Ida
name: Tachinardi, Umberto
name: Waitman, Lemuel R.
name: Wright, Rosalind J.
name: Zai, Adrian H.
name: Zheng, Kai
name: Lee, Sandra Soo-Jin
name: Malin, Bradley A.
name: Natarajan, Karthik
name: Price, W. Nicholson
name: Zhang, Rui
name: Zhang, Yiye
name: Xu, Hua
name: Bian, Jiang
name: Weng, Chunhua
name: Peng, Yifan
Publisher: name: arXiv
Year: 2024
Subjects: subject: Computers and Society (cs.CY)
subject: Artificial Intelligence (cs.AI)
subject: Human-Computer Interaction (cs.HC)
subject: FOS: Computer and information sciences
subject: FOS: Computer and information sciences

description: 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 Clinical and Translational Science Award (CTSA) Program led by the National Center for Advancing Translational Sciences (NCATS) of the National Institutes of Health (NIH) at the United States. With the rapid advancement of GenAI technologies, including large language models (LLMs), healthcare institutions face unprecedented opportunities and challenges. This research explores the current status of GenAI integration, focusing on stakeholder roles, governance structures, and ethical considerations by administering a survey among leaders of health institutions (i.e., representing academic medical centers and health systems) to assess the institutional readiness and approach towards GenAI adoption. Key findings indicate a diverse range of institutional strategies, with most organizations in the experimental phase of GenAI deployment. The study highlights significant variations in governance models, with a strong preference for centralized decision-making but notable gaps in workforce training and ethical oversight. Moreover, 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 also reveals concerns regarding GenAI bias, data security, and stakeholder trust, which must be addressed to ensure the ethical and effective implementation of GenAI technologies. This study offers valuable insights into the challenges and opportunities of GenAI integration in healthcare, providing a roadmap for institutions aiming to leverage GenAI for improved quality of care and operational efficiency.