Articlenpj health systems2025
Environment scan of generative AI infrastructure for clinical and translational science.
Article in npj health systems, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
8 citing papers in PubMed.
- The bottleneck was never data or algorithms: building a learning utility for AI-enabled learning health systems.npj health systems · 2026Article
- Narrative review of the ethics of artificial intelligence: are we ready for artificial intelligence in surgery?Journal of thoracic disease · 2026Review
- Federated knowledge retrieval elevates large language model performance on biomedical benchmarks.GigaScience · 2026Article
- Induced pluripotent stem cell reprogramming: methodological evolution and challenges in clinical translation.Frontiers in cell and developmental biology · 2026Review
- Toward governance of artificial intelligence in pediatric healthcare.NPJ digital medicine · 2025Review
- Utilization of Generative AI-drafted Responses for Managing Patient-Provider Communication.NPJ digital medicine · 2025Article
- Evolving Perceptions and Attitudes to Adopting Generative AI in Professional Settings: Multicenter Longitudinal Qualitative Study of Senior Chinese Hospital Leaders.Journal of medical Internet research · 2025Article
- Making shiny objects illuminating: the promise and challenges of large language models in U.S. health systems.npj health systems · 2025Article
Corrections and comments
- Update of
Authors and funding
57 authors.
Funding
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.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.