Beyond Multiple-Choice Accuracy: Real-World Challenges of Implementing Large Language Models in Healthcare

TL;DR

This paper discusses the real-world challenges of deploying Large Language Models in healthcare, highlighting operational, ethical, performance, and legal issues that must be addressed for responsible use.

Contribution

It provides a comprehensive analysis of the key obstacles faced when implementing LLMs in medical settings, emphasizing the need for careful consideration of multiple aspects.

Findings

Identifies operational vulnerabilities of LLMs in healthcare

Highlights ethical and social considerations for deployment

Discusses legal and regulatory compliance challenges

Abstract

Large Language Models (LLMs) have gained significant attention in the medical domain for their human-level capabilities, leading to increased efforts to explore their potential in various healthcare applications. However, despite such a promising future, there are multiple challenges and obstacles that remain for their real-world uses in practical settings. This work discusses key challenges for LLMs in medical applications from four unique aspects: operational vulnerabilities, ethical and social considerations, performance and assessment difficulties, and legal and regulatory compliance. Addressing these challenges is crucial for leveraging LLMs to their full potential and ensuring their responsible integration into healthcare.