Dynamic Decision Support Systems: Utilizing GPT-Powered Language Models in Healthcare Settings

EasyChair Preprint 12965

Abstract

Dynamic Decision Support Systems (DDSS) represent a pivotal advancement in healthcare, leveraging the capabilities of GPT (Generative Pre-trained Transformer) powered language models to enhance clinical decision-making processes. This article explores the transformative potential of DDSS in healthcare settings, elucidating how GPT-driven language models facilitate real-time analysis of complex medical data and provide personalized insights to healthcare professionals. 
The paper begins by delineating the foundational principles of DDSS and the underlying technology of GPT-powered language models. It elucidates how these models, pre-trained on vast corpora of medical literature and patient data, excel in understanding and generating human- like text, thereby enabling sophisticated analysis and interpretation of clinical information. 
Furthermore, the article investigates the multifaceted applications of DDSS across various healthcare domains, including diagnosis, treatment planning, patient monitoring, and research. By harnessing the power of natural language processing, GPT-driven language models empower healthcare providers to access timely and contextually relevant information, facilitating more informed clinical decision-making and personalized patient care.

Keyphrases: Decision Support Systems, GPT, Healthcare

@booklet{EasyChair:12965,
  author    = {Shophia Lorriane},
  title     = {Dynamic Decision Support Systems: Utilizing GPT-Powered Language Models in Healthcare Settings},
  howpublished = {EasyChair Preprint 12965},
  year      = {EasyChair, 2024}}