Harnessing AI for Personalized Medicine: GPT-Driven Models in Tailoring Healthcare Responses

EasyChair Preprint 12962

Abstract

The intersection of artificial intelligence (AI) and healthcare has ushered in a new era of personalized medicine, promising tailored treatment strategies that account for individual variability. This article explores the utilization of AI, particularly GPT (Generative Pre-trained Transformer) driven models, in revolutionizing healthcare responses. By leveraging vast amounts of medical data, GPT-driven models demonstrate remarkable capabilities in interpreting complex patient information and predicting treatment outcomes with unprecedented accuracy. 
This paper provides a comprehensive overview of the application of GPT-driven models in various aspects of personalized medicine, ranging from diagnosis and prognosis to treatment selection and monitoring. Through natural language processing (NLP) techniques, these models excel in extracting valuable insights from electronic health records, genetic profiles, and other sources of medical data, thereby enabling healthcare professionals to make well-informed decisions tailored to each patient's unique characteristics. 
Furthermore, the article delves into the challenges and opportunities associated with the integration of GPT-driven models into clinical practice. Ethical considerations, data privacy concerns, and the need for robust validation processes emerge as critical issues that must be addressed to ensure the responsible deployment of AI in healthcare settings. Additionally, the potential of GPT-driven models to enhance patient engagement and promote proactive health management is discussed, highlighting the transformative impact of AI on the future of healthcare delivery.

Keyphrases: Al, ChatGPT, Healthcare Responses

@booklet{EasyChair:12962,
  author    = {Shophia Lorriane},
  title     = {Harnessing AI for Personalized Medicine: GPT-Driven Models in Tailoring Healthcare Responses},
  howpublished = {EasyChair Preprint 12962},
  year      = {EasyChair, 2024}}