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  1. Home
  2. Research Domains
  3. Information And Computing Sciences
  4. Artificial Intelligence
  5. Natural Language Processing
  6. Large Language Models For Global Health Clinics: Opportunities And Challenges

Large Language Models for Global Health Clinics: Opportunities and Challenges

Satvik Tripathi1, Dana Alkhulaifat2, Meghana Muppuri3

  • 1Center for Global and Population Health Research in Radiology, Department of Radiology, Perelman School of Medicine at University of Pennsylvania, Philadelphia, Pennsylvania; Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts.

Journal of the American College of Radiology : JACR
|April 9, 2025

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View abstract on PubMed

Summary
This summary is machine-generated.

Large language models (LLMs) can transform health care communication and research. Addressing challenges like bias and privacy is crucial for equitable global health advancement.

Area of Science:

  • Artificial Intelligence in Healthcare
  • Health Informatics
  • Digital Health

Background:

  • Large language models (LLMs) represent a significant advancement in artificial intelligence.
  • LLMs offer potential to revolutionize health care communication, research, and decision-making.
  • The integration of LLMs into health care systems is rapidly evolving.

Purpose of the Study:

  • To explore the transformative potential of LLMs in health care.
  • To identify the challenges associated with LLM implementation in health care.
  • To propose strategies for maximizing LLM benefits while mitigating risks.

Main Methods:

  • Analysis of current LLM capabilities and applications in health care.
  • Identification of key challenges including data bias, privacy, and digital literacy.
Keywords:
Large language modelsartificial intelligenceglobal health

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  • Review of ethical considerations and potential solutions.
  • Main Results:

    • LLMs demonstrate significant potential for rapid health information dissemination and overcoming language barriers.
    • Challenges include inherent data biases, privacy concerns, and varying digital literacy levels.
    • LLMs possess strong analytical capabilities for informing health policies and clinical practices.

    Conclusions:

    • LLMs can significantly advance global health outcomes and promote health equity.
    • Developing robust ethical frameworks and bias mitigation strategies is essential.
    • Educational initiatives are needed to ensure equitable access and effective utilization of LLMs in health care worldwide.