Large Language Models in the Medical Field: Principles and Applications

Xi Chen

doi:10.62051/ijcsit.v2n3.24

Authors

Xi Chen

DOI:

https://doi.org/10.62051/ijcsit.v2n3.24

Keywords:

Large Language Models; Healthcare; Medical Decision-Making; Healthcare Innovation; Principle

Abstract

Large language models (LLMs) have emerged as powerful tools in various fields, including healthcare. This paper explores the transformative role of LLMs in healthcare quality enhancement, their applications in medical decision-making, and their potential to drive healthcare innovation. Adopting a method of case study, the present study demonstrates how LLMs streamline medical processes, assist in diagnosis and treatment, and enable personalized healthcare solutions. Additionally, the principles of LLMs in medicine were discussed, including pre-training, fine-tuning, and prompt engineering. By leveraging LLMs, healthcare professionals can enhance patient care, optimize workflows, and make more informed decisions, ultimately leading to better healthcare outcomes.

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