大语言模型与多模态模型结合临床大数据在临床医学中的应用与挑战

The application and challenges of big language model and multimodal model combined with clinical big data in clinical medicine

关键词:  大语言模型  多模态模型  临床大数据  医学辅助决策  个性化医疗

Key words:  big language model  multimodal model  clinical big data  assisted medical decision-making  personalized medicine

摘要:

【】近年来,大语言模型(LLMs)和多模态模型(MMLs)在人工智能领域取得重要进展,并在临床医学中展现出广泛应用潜力。通过整合电子病例、医学影像、基因组数据等多模态信息,这些技术显著提升了临床数据处理效率和决策支持能力。LLMs 擅长医学文本数据的分析与生成,可用于病历、医学问答和诊断建议；MMLs 则通过融合多模态数据,推动疾病诊断、个性化治疗方案制定及慢性病管理的精准化发展。本文总结了 LLMs 和 MMLs 在临床诊断、个性化医疗和慢性病管理中的具体应用,探讨了其在疾病预测、药物优化、健康监测和生活方式指导等方面的潜力。同时,分析了当前面临的挑战,包括数据质量、多模态数据融合复杂性、模型可解释性不足以及隐私保护和临床部署障碍。未来,随着技术优化、数据共享机制完善及政策法规支持,这些模型将进一步推动医学智能化发展,为提升医疗服务效率和质量提供强有力的技术支撑。

Abstract:

In recent years, large language models (LLMs) and multimodal models (MMLs) have made important progress in the field of artificial intelligence, and have shown wide application potential in clinical medicine. By integrating multimodal information such as electronic cases, medical images, and genomic data, these technologies have significantly improved the efficiency of clinical data processing and decision support capabilities. LLMs are good at the analysis and generation of medical text data, which can be used for medical record summary, medical question answering and diagnostic suggestions. MMLs promote the precise development of disease diagnosis, personalized treatment plan formulation and chronic disease management by integrating multimodal data. This review summarizes the specific applications of LLMs and MMLs in clinical diagnosis, personalized medicine and chronic disease management, and discusses their potential in disease prediction, drug optimization, health monitoring and lifestyle guidance. At the same time, the current challenges are analyzed, including data quality, multi-modal data fusion complexity, insufficient model interpretability, and privacy protection and clinical deployment barriers. In the future, with the optimization of technology, the improvement of data sharing mechanism and the support of policies and regulations, these models will further promote the development of medical intelligence and provide strong technical support for improving the efficiency and quality of medical services.