Multimodal medical data fusion has emerged as a transformative approach in smart healthcare, enabling a comprehensive understanding of patient health and personalized treatment plans. In this paper, a journey from data to information to knowledge to wisdom (DIKW) is explored through multimodal fusion for smart healthcare. We present a comprehensive review of multimodal medical data fusion focused on the integration of various data modalities. The review explores different approaches such as feature selection, rule-based systems, machine learning, deep learning, and natural language processing, for fusing and analyzing multimodal data. This paper also highlights the challenges associated with multimodal fusion in healthcare. By synthesizing the reviewed frameworks and theories, it proposes a generic framework for multimodal medical data fusion that aligns with the DIKW model. Moreover, it discusses future directions related to the four pillars of healthcare: Predictive, Preventive, Personalized, and Participatory approaches. The components of the comprehensive survey presented in this paper form the foundation for more successful implementation of multimodal fusion in smart healthcare. Our findings can guide researchers and practitioners in leveraging the power of multimodal fusion with the state-of-the-art approaches to revolutionize healthcare and improve patient outcomes.

翻译：多模态医学数据融合已成为智能医疗中的变革性方法，能够全面理解患者健康状况并制定个性化治疗方案。本文通过多模态融合探索了从数据到信息、知识再到智慧（DIKW）的演进路径。我们围绕多种数据模态的集成，对多模态医学数据融合进行了全面综述，涵盖了特征选择、规则系统、机器学习、深度学习及自然语言处理等融合与分析多模态数据的不同方法。本文还强调了医疗领域多模态融合面临的挑战。通过综合现有框架与理论，我们提出了一个与DIKW模型相一致的多模态医学数据融合通用框架。此外，本文探讨了与医疗四大支柱（预测性、预防性、个性化与参与性方法）相关的未来方向。本综述所涵盖的各组成部分为多模态融合在智能医疗中的成功实施奠定了基础。我们的发现可指导研究人员和从业者利用最新技术手段充分发挥多模态融合的潜力，从而革新医疗模式并改善患者预后。