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A Review of Large Language Models in Medicine: Applications, Challenges, and Future Directions

Authors :

Elham Shameli¹ Seyed Mohsen Mirhosseini²

1- گروه مهندسی کامپیوتر، دانشگاه آزاد اسلامی، کرج، ایران 2- گروه مهندسی کامپیوتر، دانشگاه آزاد اسلامی، کرج، ایران

Keywords :

Large Language Models،Natural Language Processing،Medicine،ChatGPT

Abstract :

The rapid advancement of Large Language Models (LLMs) has opened up new horizons for their application in medicine. This review explores the transformative impact of LLMs across various domains of the healthcare system. By examining the evolution and underlying architectures of LLMs, this article lays a foundation for understanding their potential integration into clinical practice. It further explores key applications, including medical assistants, text summarization, medical education, disease diagnosis and prediction, question answering, and drug discovery. These applications reflect LLMs' potential to enhance work efficiency, support clinical decision-making, and enable personalization in both medical education and therapeutic management. Nevertheless, several challenges remain that must be addressed by future research. These include hallucinations that may lead to misinformation, limitations in domain-specific medical datasets, lack of robust evaluation metrics, ethical and legal complexities, difficulties in keeping model knowledge bases current, the need to align model behavior with stringent medical standards, and broader concerns regarding security and regulation. This in-depth review provides an analytical perspective on the transformative potential of LLMs in medicine, carefully examining the multifaceted dimensions of their implementation.