系统梳理医学多模态数据建模的挑战与解决路径
Challenges and proposed solutions in modeling multimodal medical data: A systematic review
- 归纳69项研究，识别数据缺失、样本量小等核心难题
- 提出迁移学习、注意力机制等方法提升融合效果
- 适合医疗AI研究者参考，助力临床决策系统开发
多模态数据建模已成为临床研究的重要方法，可整合影像、基因组、可穿戴设备和电子健康记录等多种数据。尽管其有望提升诊断准确率并支持个性化诊疗，但异构数据建模仍面临诸多技术挑战。本系统综述分析了69项研究，揭示了常见障碍，包括模态缺失、样本量有限、维度不平衡、可解释性差及融合策略选择困难。文章重点介绍了近期进展，如迁移学习、生成模型、注意力机制和神经架构搜索等方法所提供的有效解决方案。通过梳理当前趋势与创新，本文为医学多模态建模领域提供全面概览，并为未来研究与应用提供实践指导。
原文摘要 · Abstract (English)
Multimodal data modeling has emerged as a powerful approach in clinical research, enabling the integration of diverse data types such as imaging, genomics, wearable sensors, and electronic health records. Despite its potential to improve diagnostic accuracy and support personalized care, modeling such heterogeneous data presents significant technical challenges. This systematic review synthesizes findings from 69 studies to identify common obstacles, including missing modalities, limited sample sizes, dimensionality imbalance, interpretability issues, and finding the optimal fusion techniques. We highlight recent methodological advances, such as transfer learning, generative models, attention mechanisms, and neural architecture search that offer promising solutions. By mapping current trends and innovations, this review provides a comprehensive overview of the field and offers practical insights to guide future research and development in multimodal modeling for medical applications.
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