Exploring the Impact of Large Language Models on Disease Diagnosis

Abstract

The emergence of large language models (LLMs) has revolutionized various fields, including education, finance, marketing, healthcare, and medicine. In this review, we aim to explore the application of LLMs in the healthcare sector, with a specific focus on disease diagnostics. The review highlighted the widespread use of LLMs, such as GPT-4, ChatGPT, GPT-3.5, and LLaMA, with GPT-4 being the most frequently used in disease diagnostics due to its diverse applications, improved accuracy, and efficiency. This review shows that LLMs have utilized a variety of medical data sources, including general medical databases, specialized documents, medical images, and genomic data. Moreover, the focus of these LLMs spans a broad spectrum of healthcare fields, addressing chronic conditions, respiratory diseases, cancer, and rare diseases. The performance evaluation of LLMs involves both qualitative and quantitative measures assessing their diagnostic accuracy. The findings highlight the evolving nature of LLMs in improving diagnostic accuracy.

Keywords

• Large language models
• health care applications
• ChatGPT
• GPT-4
• GPT-3.5
• BARD