LLM从文本到多模态，重塑医疗实践。
From Text to Multimodality: Exploring the Evolution and Impact of Large Language Models in Medical Practice
- 将文本、图像、音频等多源数据融合，提升医疗洞察力。
- 在临床决策、医学影像等领域已展现实用价值。
- 适合关注AI医疗落地的医生与研究人员参考。
大型语言模型（LLMs）已迅速从纯文本系统演变为多模态平台，在医疗等领域产生深远影响。本文全面综述了LLMs向多模态大型语言模型（MLLMs）的演进及其在医疗实践中的日益增长作用。我们分析了当前医疗领域MLLMs的应用现状，涵盖临床决策支持、医学影像、患者互动及科研应用。研究强调了MLLMs在整合文本、图像、音频等多种数据类型方面的独特能力，从而提供更全面的患者健康洞察。同时，文章讨论了其实际部署面临的数据限制、技术挑战与伦理问题。通过识别关键研究空白，本文旨在引导未来在数据集构建、模态对齐方法及伦理规范建立等方面的研究方向。随着MLLMs持续塑造医疗未来，理解其潜力与局限对于实现负责任且高效的医疗融合至关重要。
原文摘要 · Abstract (English)
Large Language Models (LLMs) have rapidly evolved from text-based systems to multimodal platforms, significantly impacting various sectors including healthcare. This comprehensive review explores the progression of LLMs to Multimodal Large Language Models (MLLMs) and their growing influence in medical practice. We examine the current landscape of MLLMs in healthcare, analyzing their applications across clinical decision support, medical imaging, patient engagement, and research. The review highlights the unique capabilities of MLLMs in integrating diverse data types, such as text, images, and audio, to provide more comprehensive insights into patient health. We also address the challenges facing MLLM implementation, including data limitations, technical hurdles, and ethical considerations. By identifying key research gaps, this paper aims to guide future investigations in areas such as dataset development, modality alignment methods, and the establishment of ethical guidelines. As MLLMs continue to shape the future of healthcare, understanding their potential and limitations is crucial for their responsible and effective integration into medical practice.
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