Large Language Models for Medicine: A Survey

TL;DR

This survey reviews the development, applications, challenges, and future research directions of large language models in medicine, highlighting their potential to transform medical practices and the need for technical integration to address existing challenges.

Contribution

It provides a comprehensive overview of medical LLMs, analyzing their current state, challenges, and future research directions, which is a novel synthesis for the field.

Findings

Medical LLMs have broad applications in healthcare.

Challenges include data privacy, model interpretability, and integration issues.

Future research should focus on technical solutions to these challenges.

Abstract

To address challenges in the digital economy's landscape of digital intelligence, large language models (LLMs) have been developed. Improvements in computational power and available resources have significantly advanced LLMs, allowing their integration into diverse domains for human life. Medical LLMs are essential application tools with potential across various medical scenarios. In this paper, we review LLM developments, focusing on the requirements and applications of medical LLMs. We provide a concise overview of existing models, aiming to explore advanced research directions and benefit researchers for future medical applications. We emphasize the advantages of medical LLMs in applications, as well as the challenges encountered during their development. Finally, we suggest directions for technical integration to mitigate challenges and potential research directions for the future of medical LLMs, aiming to meet the demands of the medical field better.