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

Junwei Liu1, Xiaoping Cen1,2,3, Chenxin Yi1,4

  • 1Guangzhou National Laboratory, Guangzhou 510005, China.

Genomics, Proteomics & Bioinformatics
|March 4, 2025
PubMed
Summary
This summary is machine-generated.

Artificial intelligence (AI) and deep learning models are revolutionizing biomedical data analysis, enhancing disease diagnosis and treatment design. This review explores AI applications in integrating diverse biomedical data, addressing challenges, and proposing future research directions.

Keywords:
Biomedical analysisLarge language modelMeta-learningModel interpretationMultimodal learning

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Area of Science:

  • Biomedical Informatics
  • Artificial Intelligence
  • Data Science

Background:

  • Biomedical examination methods generate vast, diverse personal datasets (molecular, cellular, imaging, EHRs).
  • Integrating these multimodal datasets is crucial for precise disease diagnosis, biomarker discovery, and personalized treatment design.
  • Artificial intelligence (AI), especially deep learning, shows promise in enhancing precision, efficiency, and generalization in biomedical applications.

Purpose of the Study:

  • To provide a comprehensive overview of AI in integrative biomedical data analysis.
  • To discuss challenges in learning multimodal biomedical datasets and their integration into clinical practice.
  • To propose future research directions for AI in advancing biomedical research and clinical applications.

Main Methods:

  • Review of existing literature on AI, deep learning, and multimodal representation learning in biomedicine.
  • Analysis of applications of AI in integrating diverse biomedical data modalities.
  • Discussion of challenges including data privacy, fusion, and model interpretability.

Main Results:

  • AI, including large language and vision models, significantly extends biomedical applications.
  • Deep learning models offer increased precision, efficiency, and generalization for complex biomedical data.
  • Key challenges involve data privacy, effective data fusion, and model interpretability.

Conclusions:

  • AI and deep learning are powerful tools for analyzing integrated biomedical data.
  • Addressing challenges in data privacy, fusion, and interpretation is vital for clinical translation.
  • Future directions include model pre-training and knowledge integration to enhance AI's impact on biomedical research and healthcare.