Challenges in AI-driven Biomedical Multimodal Data Fusion and Analysis.

Junwei Liu 刘俊伟, Xiaoping Cen 岑萧萍, Chenxin Yi 伊晨昕, Feng-Ao Wang 王烽傲, Junxiang Ding 丁俊翔, Jinyu Cheng 程瑾瑜, Qinhua Wu 吴沁桦, Baowen Gai 盖宝文, Yiwen Zhou 周奕雯, Ruikun He 贺瑞坤, Feng Gao 高峰, Yixue Li 李亦学

Journal: Genomics, proteomics & bioinformatics 2025;23(1):

PMID: 40036568

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Abstract

The rapid development of biological and medical examination methods has vastly expanded personal biomedical information, including molecular, cellular, image, and electronic health record datasets. Integrating this wealth of information enables precise disease diagnosis, biomarker identification, and treatment design in clinical settings. Artificial intelligence (AI) techniques, particularly deep learning models, have been extensively employed in biomedical applications, demonstrating increased precision, efficiency, and generalization. The success of the large language and vision models further significantly extends their biomedical applications. However, challenges remain in learning these multimodal biomedical datasets, such as data privacy, fusion, and model interpretation. In this review, we provide a comprehensive overview of various biomedical data modalities, multimodal representation learning methods, and the applications of AI in biomedical data integrative analysis. Additionally, we discuss the challenges in applying these deep learning methods and how to better integrate them into biomedical scenarios. We then propose future directions for adapting deep learning methods with model pretraining and knowledge integration to advance biomedical research and benefit their clinical applications.

© The Author(s) 2025. Published by Oxford University Press and Science Press on behalf of the Beijing Institute of Genomics, Chinese Academy of Sciences / China National Center for Bioinformation and Genetics Society of China.

Address: Guangzhou National Laboratory, Guangzhou 510005, China.; Guangzhou National Laboratory, Guangzhou 510005, China.; College of Life Sciences, University of Chinese Academy of Sciences, Beijing 100049, China.; HIM-BGI Omics Center, Hangzhou Institute of Medicine, Chinese Academy of Sciences, Hangzhou 310022, China.; Guangzhou National Laboratory, Guangzhou 510005, China.; School of Intelligent Systems Engineering, Sun Yat-sen University, Shenzhen 518107, China.; Guangzhou National Laboratory, Guangzhou 510005, China.; Key Laboratory of Systems Health Science of Zhejiang Province, School of Life Science, Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Hangzhou 310024, China.; Guangzhou National Laboratory, Guangzhou 510005, China.; Multimedia Laboratory, The Chinese University of Hong Kong, Hong Kong Special Administrative Region 999077, China.; Department of Colorectal Surgery, The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou 510655, China.; Biomedical Innovation Center, The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou 510655, China.; School of Electronics and Communication Engineering, Sun Yat-sen University, Shenzhen 518107, China.; BYHEALTH Institute of Nutrition & Health, Guangzhou 510663, China.; Department of Colorectal Surgery, The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou 510655, China.; Biomedical Innovation Center, The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou 510655, China.; Shanghai Artificial Intelligence Laboratory, Shanghai 200433, China.; Guangzhou National Laboratory, Guangzhou 510005, China.; Key Laboratory of Systems Health Science of Zhejiang Province, School of Life Science, Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Hangzhou 310024, China.; GZMU-GIBH Joint School of Life Sciences, The Guangdong-Hong Kong-Macau Joint Laboratory for Cell Fate Regulation and Diseases, Guangzhou Medical University, Guangzhou 511436, China.; School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, China.; Shanghai Institute of Nutrition and Health, Chinese Academy of Sciences, Shanghai 200030, China.; Collaborative Innovation Center for Genetics and Development, Fudan University, Shanghai 200433, China.; Shanghai Institute for Biomedical and Pharmaceutical Technologies, Shanghai 200032, China.

DOI: 10.1093/gpbjnl/qzaf011

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MeSH Terms: Humans, Artificial Intelligence, Deep Learning, Biomedical Research