Challenges in AI-driven Biomedical Multimodal Data Fusion and Analysis
Chinese Academy of Sciences · University of Chinese Academy of Sciences · +10 more institutions
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…
Citation impact
- FWCI
- 82.40
- Percentile
- 100%
- References
- 164
Authors
12- JLJunwei LiuCorresponding
- XCXiaoping Cen
Chinese Academy of Sciences, University of Chinese Academy of Sciences
- CYChenxin Yi
Sun Yat-sen University
- FWFeng‐ao Wang
University of Chinese Academy of Sciences
- JDJunxiang Ding
University of Chinese Academy of Sciences
Topics & keywords
- Computer science
- Fusion
- Artificial intelligence
- Computational biology
- Biology
- Philosophy