Differentiating hype from practical applications of large language models in medicine -- a primer for healthcare professionals

TL;DR

This paper discusses the potential and risks of large language models in medicine, emphasizing the need for careful application to ensure benefits without compromising safety or privacy.

Contribution

It provides a primer for healthcare professionals to differentiate between hype and practical use of LLMs in medicine, highlighting key considerations and risks.

Findings

LLMs can improve healthcare efficiency but lack understanding of objective truth.

Risks include potential disclosure of protected health information.

Careful, context-aware deployment is essential for safe use.

Abstract

The medical ecosystem consists of the training of new clinicians and researchers, the practice of clinical medicine, and areas of adjacent research. There are many aspects of these domains that could benefit from the application of task automation and programmatic assistance. Machine learning and artificial intelligence techniques, including large language models (LLMs), have been promised to deliver on healthcare innovation, improving care speed and accuracy, and reducing the burden on staff for manual interventions. However, LLMs have no understanding of objective truth that is based in reality. They also represent real risks to the disclosure of protected information when used by clinicians and researchers. The use of AI in medicine in general, and the deployment of LLMs in particular, therefore requires careful consideration and thoughtful application to reap the benefits of these technologies while avoiding the dangers in each context.