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：多模态医疗数据融合已成为智慧医疗领域的一种变革性方法，能够全面理解患者健康状况并制定个性化治疗方案。本文以多模态融合为切入点，探讨了从数据到信息、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，对多模态医疗数据融合进行了全面综述。研究涵盖了特征选择、基于规则的系统、机器学习、深度学习以及自然语言处理等不同方法，用于实现多模态数据的融合与分析。本文还强调了医疗领域中多模态融合面临的挑战。通过整合所综述的框架与理论，我们提出了一种与DIKW模型一致的多模态医疗数据融合通用框架。此外，本文探讨了与医疗四大支柱相关的未来方向，即预测性、预防性、个性化和参与性方法。本综述所呈现的各个组成部分，为在智慧医疗中更成功地实施多模态融合奠定了基础。我们的研究结果可指导研究人员和实践者利用前沿方法发挥多模态融合的潜力，从而革新医疗体系并改善患者疗效。