Utilizing multimodal artificial intelligence to advance cardiovascular diseases

Abstract

The emergence of artificial intelligence (AI) is transforming cardiovascular medicine. Initially, AI applications concentrated on analyz-ing single data types, such as electrocardiograms and imaging studies. However, advancements in multimodal AI have now enabled the integration of diverse data sources, facilitating a comprehensive understanding of patient health and predictive accuracy of dis-ease outcomes. In this review, we discuss current achievements in multimodal AI within cardiovascular medicine, including various combinations of different modalities, computer algorithms of data integration and fusion, and their integration into clinical workflow. As the field continues to evolve, we further propose current challenges and prospects for their future implementation.

Keywords

artificial intelligence / multimodal / cardiovascular medicine / cardiology

Acknowledgments

Figures 4 and 5 were created with Figdraw image and illustration software.

Author contributions

X.Y., Y.L., and J.W. wrote the original article, X.Y. and M.C. conceived the original ideas, M.C and Z.Y. supervised the manuscript, X.Y. and Y.J. completed visualization, M.C. and Y.L. helped with revision and editing, M.C. and J.W. completed funding acquisition. All authors read and approved the final manuscript and participated in final approval of the version to be published.

Conflict of interest

None declared.

Funding

This work was supported by the National Natural Science Foundation of China (grant Nos. 62306192, U23A20395, 82170375, and 62476185), 1.3.5 project for disciplines of excellence from West China Hospital of Sichuan University (grant Nos. ZYGD23021 and 23HXFH009), and the Natural Science Foundation of Sichuan Province, China (grant No. 2023NSFSC1638).

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