Teoh Jing Ru, Dong Jian, Zuo Xiaowei, Lai Khin Wee, Hasikin Khairunnisa, Wu Xiang
Department of Biomedical Engineering, University of Malaya, Kuala Lumpur, Malaysia.
China Electronics Standardization Institute, Beijing, China.
PeerJ Comput Sci. 2024 Oct 30;10:e2298. doi: 10.7717/peerj-cs.2298. eCollection 2024.
With the increasing availability of diverse healthcare data sources, such as medical images and electronic health records, there is a growing need to effectively integrate and fuse this multimodal data for comprehensive analysis and decision-making. However, despite its potential, multimodal data fusion in healthcare remains limited. This review paper provides an overview of existing literature on multimodal data fusion in healthcare, covering 69 relevant works published between 2018 and 2024. It focuses on methodologies that integrate different data types to enhance medical analysis, including techniques for integrating medical images with structured and unstructured data, combining multiple image modalities, and other features. Additionally, the paper reviews various approaches to multimodal data fusion, such as early, intermediate, and late fusion methods, and examines the challenges and limitations associated with these techniques. The potential benefits and applications of multimodal data fusion in various diseases are highlighted, illustrating specific strategies employed in healthcare artificial intelligence (AI) model development. This research synthesizes existing information to facilitate progress in using multimodal data for improved medical diagnosis and treatment planning.
随着各种医疗保健数据源（如医学图像和电子健康记录）的可用性不断提高，有效地整合和融合这些多模态数据以进行全面分析和决策的需求也日益增长。然而，尽管多模态数据融合具有潜力，但在医疗保健领域的应用仍然有限。这篇综述文章概述了医疗保健领域中多模态数据融合的现有文献，涵盖了2018年至2024年间发表的69篇相关著作。它重点关注整合不同数据类型以加强医学分析的方法，包括将医学图像与结构化和非结构化数据进行整合、结合多种图像模态以及其他特征的技术。此外，本文还综述了多模态数据融合的各种方法，如早期、中期和晚期融合方法，并探讨了与这些技术相关的挑战和局限性。强调了多模态数据融合在各种疾病中的潜在益处和应用，阐述了医疗人工智能（AI）模型开发中采用的具体策略。本研究综合现有信息，以推动利用多模态数据改进医学诊断和治疗规划的进展。
