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Related Concept Videos

Issues And Trends In Healthcare Delivery System01:29

Issues And Trends In Healthcare Delivery System

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The issues and trends in healthcare delivery are constantly changing. The COVID-19 pandemic is one recent issue that wreaked havoc on healthcare systems, causing a shortage of healthcare workers, high demand for medicines and supplies, and increased medical expenditure due to a lack of insurance. Other issues include rising healthcare costs and care fragmentation.
Cost Containment
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Related Experiment Video

Updated: Jan 16, 2026

Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System
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Published on: July 11, 2025

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Environment Scan of Generative AI Infrastructure for Clinical and Translational Science.

Betina Idnay1, Zihan Xu2, William G Adams3

  • 1Department of Biomedical Informatics, Columbia University Irving Medical Center, New York, NY, USA.

Arxiv
|October 1, 2025
PubMed
Summary
This summary is machine-generated.

Generative AI (GenAI) adoption in clinical and translational science is in the early stages, with most institutions experimenting. Key challenges include workforce training, ethical oversight, and addressing bias for effective healthcare integration.

Keywords:
Clinical and Translational ResearchGenAILLM

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Area of Science:

  • Healthcare Informatics
  • Artificial Intelligence in Medicine
  • Translational Science

Background:

  • Generative AI (GenAI) and large language models (LLMs) present significant opportunities and challenges for healthcare.
  • The Clinical and Translational Science Award (CTSA) network comprises 36 institutions actively exploring GenAI integration.

Purpose of the Study:

  • To conduct an environmental scan of GenAI infrastructure within the national CTSA network.
  • To assess institutional readiness, stakeholder roles, governance, and ethical considerations for GenAI adoption in healthcare.

Main Methods:

  • A survey was administered to leaders of academic medical centers and health systems within the CTSA network.
  • The survey focused on current GenAI deployment status, governance models, and identified challenges.

Main Results:

  • Most institutions are in the experimental phase of GenAI deployment, exhibiting diverse strategies.
  • Centralized decision-making is preferred, but significant gaps exist in workforce training and ethical oversight.
  • Concerns regarding GenAI bias, data security, and stakeholder trust were identified.

Conclusions:

  • GenAI integration in healthcare requires a coordinated approach involving senior leaders, clinicians, IT staff, and researchers.
  • Addressing ethical concerns and ensuring robust governance are crucial for the effective implementation of GenAI.
  • This study provides insights and a roadmap for healthcare institutions leveraging GenAI to enhance care quality and operational efficiency.