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    Area of Science:

    • Biomedical Informatics
    • Computational Biology
    • Precision Medicine

    Background:

    • Novel biomedical technologies generate vast multi-modal data, including multi-omics and bio-signals.
    • These data hold potential for realizing precision medicine but present integration challenges.
    • Artificial intelligence (AI) is crucial for data-driven knowledge discovery and causal inference.

    Approach:

    • This review summarizes state-of-the-art AI models for integrating multi-omics data with electronic health records (EHRs).
    • We discuss current methodologies and their applications in precision medicine.
    • The focus is on AI-driven integration strategies for complex biomedical datasets.

    Key Points:

    • AI models show promise in leveraging multi-modal data for healthcare applications.
    • Effective integration of multi-omics and EHR data is key to advancing precision medicine.
    • Challenges include data volume, complexity, and interpretability of AI models.

    Conclusions:

    • AI offers powerful tools for unlocking insights from integrated biomedical data.
    • Further research is needed to overcome challenges in multi-modal data integration for precision medicine.
    • This review aims to guide future research and development in AI for precision medicine.