Promise and Challenges of Generative AI in Healthcare Information Systems

Abstract

Large Language Models (LLMs) based on pretrained transformer architectures, such as Generative Pretrained Transformer 4 (GPT-4) from OpenAI, are on the cutting age of artificial intelligence research. Along with generating abundant academic literature, these models are the basis of numerous practical systems widely utilized by end users and organizations. In healthcare information systems, there are many case studies and research prototypes demonstrating the promise of applying GPT-like programs to numerous practical natural language processing tasks. At the same time, current limitations of LLMs prevent their safe deployments in professional environments. In this study, we give an overview of capabilities, limitations, and risks associated with current iterations of LLMs. We provide an overview of literature on using LLMs in healthcare context. Finally, we present a framework of generic healthcare IT system utilizing LLMs, and discuss avenues for future research.

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