系统梳理医学大模型进展，揭示技术路径与应用前景
Advances in Large Language Models for Medicine
- 按训练方法将医学大模型分为三类，评估方式分两类
- 分析医疗大模型在临床场景中的适配性与实际效果
- 适合医学AI研究者与临床智能化推动者参考
人工智能技术近年快速进步，大语言模型（LLMs）成为重要突破。LLMs在多个领域广泛应用，尤其在医疗领域表现突出。本文系统回顾了当前医学大模型的研究进展，深入分析大型医学模型的训练技术、在医疗场景中的适配性、相关应用，以及其优势与局限。创新性地根据训练方法将医学大模型分为三类，并将评估方法划分为两类。最后，针对现有挑战提出解决方案，并展望未来研究方向。通过系统梳理已有及前沿研究成果，旨在强调发展医学大模型的必要性，深化对其当前发展状态的理解，并为后续研究提供明确指引。
原文摘要 · Abstract (English)
Artificial intelligence (AI) technology has advanced rapidly in recent years, with large language models (LLMs) emerging as a significant breakthrough. LLMs are increasingly making an impact across various industries, with the medical field standing out as the most prominent application area. This paper systematically reviews the up-to-date research progress of LLMs in the medical field, providing an in-depth analysis of training techniques for large medical models, their adaptation in healthcare settings, related applications, as well as their strengths and limitations. Furthermore, it innovatively categorizes medical LLMs into three distinct types based on their training methodologies and classifies their evaluation approaches into two categories. Finally, the study proposes solutions to existing challenges and outlines future research directions based on identified issues in the field of medical LLMs. By systematically reviewing previous and advanced research findings, we aim to highlight the necessity of developing medical LLMs, provide a deeper understanding of their current state of development, and offer clear guidance for subsequent research.
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