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Continuous Learning in Healthcare: GPT-Enhanced Models for Evolving Medical Practices

EasyChair Preprint no. 12961

10 pagesDate: April 9, 2024


Continuous learning in healthcare is essential for staying abreast of evolving medical practices and delivering optimal patient care. This article explores the integration of Generative Pre-trained Transformers (GPT) enhanced models in healthcare, elucidating their role in facilitating continuous learning and adaptation within medical settings.  
The paper begins by emphasizing the importance of continuous learning in healthcare, highlighting the dynamic nature of medical knowledge and the need for healthcare professionals to continuously update their skills and knowledge. It introduces the concept of GPT-enhanced models and their potential to revolutionize learning and knowledge dissemination in healthcare.  
Furthermore, the article delves into the various applications of GPT-enhanced models in continuous learning, including medical education, clinical decision support, literature review, and knowledge management. By harnessing the capabilities of GPT models, healthcare professionals can access curated medical content, receive personalized learning experiences, and stay updated with the latest advancements in their field.

Keyphrases: ChatGPT, Healthcare, Medical practices

BibTeX entry
BibTeX does not have the right entry for preprints. This is a hack for producing the correct reference:
  author = {Shophia Lorriane},
  title = {Continuous Learning in Healthcare: GPT-Enhanced Models for Evolving Medical Practices},
  howpublished = {EasyChair Preprint no. 12961},

  year = {EasyChair, 2024}}
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