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Abstract
The convergence of artificial intelligence (AI) with genomics, radiology, and electronic health records (EHRs) marks a transformative era in precision medicine. Multimodal AI, which integrates data from multiple sources, enhances diagnostic accuracy, treatment personalization, and disease prediction. This paper examines the current state and future potential of multimodal AI in healthcare by synthesizing key developments, analyzing case studies, and discussing implementation challenges. The integration of heterogeneous data types leads to improved model robustness and interpretability, offering a powerful toolkit for personalized healthcare.
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