Environment Scan of Generative AI Infrastructure for Clinical and Translational Science
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 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.
Keywords
Cite
@article{arxiv.2410.12793,
title = {Environment Scan of Generative AI Infrastructure for Clinical and Translational Science},
author = {Betina Idnay and Zihan Xu and William G. Adams and Mohammad Adibuzzaman and Nicholas R. Anderson and Neil Bahroos and Douglas S. Bell and Cody Bumgardner and Thomas Campion and Mario Castro and James J. Cimino and I. Glenn Cohen and David Dorr and Peter L Elkin and Jungwei W. Fan and Todd Ferris and David J. Foran and David Hanauer and Mike Hogarth and Kun Huang and Jayashree Kalpathy-Cramer and Manoj Kandpal and Niranjan S. Karnik and Avnish Katoch and Albert M. Lai and Christophe G. Lambert and Lang Li and Christopher Lindsell and Jinze Liu and Zhiyong Lu and Yuan Luo and Peter McGarvey and Eneida A. Mendonca and Parsa Mirhaji and Shawn Murphy and John D. Osborne and Ioannis C. Paschalidis and Paul A. Harris and Fred Prior and Nicholas J. Shaheen and Nawar Shara and Ida Sim and Umberto Tachinardi and Lemuel R. Waitman and Rosalind J. Wright and Adrian H. Zai and Kai Zheng and Sandra Soo-Jin Lee and Bradley A. Malin and Karthik Natarajan and W. Nicholson Price and Rui Zhang and Yiye Zhang and Hua Xu and Jiang Bian and Chunhua Weng and Yifan Peng},
journal= {arXiv preprint arXiv:2410.12793},
year = {2025}
}