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Future-Proofing Healthcare Systems: GPT-Powered Language Models for Adaptive Responses to Emerging Health Issues

EasyChair Preprint 12979

10 pagesDate: April 9, 2024

Abstract

As healthcare systems globally face increasingly complex challenges, the need for adaptive and responsive solutions has become paramount. This paper explores the potential of GPT-powered language models in revolutionizing healthcare by enabling adaptive responses to emerging health issues. Generative Pre-trained Transformers (GPT) are at the forefront of natural language processing, capable of understanding and generating human-like text. By leveraging GPT-powered language models, healthcare systems can harness vast amounts of medical data, research literature, and patient records to provide timely insights, personalized recommendations, and dynamic decision support.  
Through a comprehensive review of literature, case studies, and expert insights, this paper examines the applications, benefits, challenges, and ethical considerations of integrating GPT-powered language models into healthcare systems. From predictive analytics and clinical decision support to patient engagement and population health management, GPT-powered solutions offer unprecedented opportunities to enhance healthcare delivery, improve patient outcomes, and address emerging health threats proactively.

Keyphrases: Emerging Health Issues, GPT-Powered Language, Healthcare systems

BibTeX entry
BibTeX does not have the right entry for preprints. This is a hack for producing the correct reference:
@booklet{EasyChair:12979,
  author    = {Shophia Lorriane},
  title     = {Future-Proofing Healthcare Systems: GPT-Powered Language Models for Adaptive Responses to Emerging Health Issues},
  howpublished = {EasyChair Preprint 12979},
  year      = {EasyChair, 2024}}
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