AI-based multimodal integration of genomics and electronic health records.

AI and machine learning frameworks are advancing multimodal integration of genomics and electronic health records, enabling scalable analysis of disease heterogeneity, biomarkers, and risk prediction.

• Why it matters: EHRs provide longitudinal, patient-specific clinical data, but combining them with genomic information is needed to better understand heterogeneity and support clinical decision-making at scale.
• What they did: This Review assesses current AI/ML approaches that integrate genomic, multi-omics, and EHR data, emphasizing progress in biobanks, data standardization, and modelling/implementation architectures.
• The result: The field is reshaping genomics research and clinical practice by improving the ability to handle high-dimensional, noisy, and irregularly timed data, while also clarifying current capabilities and limitations.

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