A Survey on Medical Large Language Models: Technology, Application, Trustworthiness, and Future Directions
Lei Liu, Xiaoyan Yang, Junchi Lei, Yue Shen, Jian Wang, Peng Wei,, Zhixuan Chu, Zhan Qin, Kui Ren

TL;DR
This survey reviews recent advances in Medical Large Language Models (Med-LLMs), highlighting their technological evolution, diverse applications, ethical challenges, and future research directions to promote responsible integration into healthcare.
Contribution
It provides a comprehensive overview of Med-LLMs, including foundational technology, applications, ethical considerations, and future prospects, filling a gap in current literature.
Findings
Med-LLMs enhance clinical decision support and medical education.
Challenges include ensuring fairness, privacy, and robustness.
Future directions involve regulatory frameworks and ethical standards.
Abstract
With the advent of Large Language Models (LLMs), medical artificial intelligence (AI) has experienced substantial technological progress and paradigm shifts, highlighting the potential of LLMs to streamline healthcare delivery and improve patient outcomes. Considering this rapid technical progress, in this survey, we trace the recent advances of Medical Large Language Models (Med-LLMs), including the background, key findings, and mainstream techniques, especially for the evolution from general-purpose models to medical-specialized applications. Firstly, we delve into the foundational technology of Med-LLMs, indicating how general models can be progressively adapted and refined for the complicated medical tasks. Secondly, the wide-ranging applications of Med-LLMs are investigated across various healthcare domains, as well as an up-to-date review of existing Med-LLMs. The transformative…
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Taxonomy
TopicsMachine Learning in Healthcare · Artificial Intelligence in Healthcare and Education
MethodsRefunds@Expedia|||How do I get a full refund from Expedia? · Attention Is All You Need · Linear Layer · Byte Pair Encoding · Adam · Residual Connection · Multi-Head Attention · Dropout · Dense Connections · Cosine Annealing
