This integration of artificial intelligence (AI) into data streams represents a paradigm shift in healthcare toward the concept of multimodal AI. Traditional AI systems are limited to single-modality analysis, such as image-only or text-only, which makes it difficult for them to understand the complex and holistic nature of human health and disease. In this chapter, we discuss how multimodal AI systems transcend this barrier by analyzing and integrating information from heterogeneous sources concurrently. The main data modalities involved include medical imaging, electronic health records, genomic sequences, clinical notes, and physiological signals. This convergence of data enables a more holistic and context-rich characterization of patient conditions. Deploying multimodal AI is expected to advance the entire clinical spectrum, from improved diagnostic precision and risk stratification to highly personalized and predictive treatment pathways. This chapter outlines the technological architecture and current research breakthroughs that drive this field. It highlights multimodal AI as the critical new frontier for revolutionizing clinical decision support and optimizing patient outcomes.