Environment scan of generative AI infrastructure for clinical and translational science

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 Nationa...

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Vydané v:NPJ Health Systems Ročník 2; číslo 1; s. 4
Hlavní autori: 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
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: London Nature Publishing Group UK 01.01.2025
Nature Publishing Group
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692/700/228
692/700/478
Decision making
Electronic health records
Ethics
Generative artificial intelligence
Health Informatics
Health Policy
Health Services Research
Infrastructure
Large language models
Leadership
Medicine
Medicine & Public Health
Patients
Public Health
Stakeholders
Strategic planning
Task forces
United States > US
Health services
Public health
ISSN:3005-1959, 3005-1959
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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.
AbstractList 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.
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.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.
ArticleNumber 4
Author Adibuzzaman, Mohammad
Zai, Adrian H.
Lai, Albert M.
Kalpathy-Cramer, Jayashree
Mirhaji, Parsa
Bahroos, Neil
Peng, Yifan
Campion, Thomas
Ferris, Todd
Li, Lang
McGarvey, Peter
Kandpal, Manoj
Osborne, John D.
Xu, Hua
Dorr, David
Lambert, Christophe G.
Cimino, James J.
Harris, Paul A.
Weng, Chunhua
Huang, Kun
Waitman, Lemuel R.
Hanauer, David
Cohen, I. Glenn
Katoch, Avnish
Zhang, Rui
Malin, Bradley A.
Lee, Sandra Soo-Jin
Elkin, Peter L.
Castro, Mario
Murphy, Shawn
Prior, Fred
Zheng, Kai
Natarajan, Karthik
Fan, Jungwei W.
Anderson, Nicholas R.
Sim, Ida
Luo, Yuan
Liu, Jinze
Mendonca, Eneida A.
Bumgardner, Cody
Idnay, Betina
Foran, David J.
Price II, W. Nicholson
Shara, Nawar
Karnik, Niranjan S.
Bell, Douglas S.
Tachinardi, Umberto
Wright, Rosalind J.
Bian, Jiang
Shaheen, Nicholas J.
Lu, Zhiyong
Zhang, Yiye
Hogarth, Mike
Lindsell, Christopher
Xu, Zihan
Adams, William G.
Paschalidis, Ioannis C.
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BackLink https://www.ncbi.nlm.nih.gov/pubmed/39872195$$D View this record in MEDLINE/PubMed
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CitedBy_id crossref_primary_10_1038_s44401_025_00014_7
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Issue 1
Keywords Health services
Public health
Language English
License The Author(s) 2025.
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692/700/478
Decision making
Electronic health records
Ethics
Generative artificial intelligence
Health Informatics
Health Policy
Health Services Research
Infrastructure
Large language models
Leadership
Medicine
Medicine & Public Health
Patients
Public Health
Stakeholders
Strategic planning
Task forces
Title Environment scan of generative AI infrastructure for clinical and translational science
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