Researcher Sasi Kumar Kolla has examined how multimodal foundation deep learning models could be designed to work across multiple healthcare data types simultaneously, addressing the gap that most artificial intelligence systems in medicine are built to interpret only one format at a time. His paper, titled *Foundation Deep Learning Models For Precision Medicine Using Multimodal Big Data*, published in the *International Journal of Advances in Signal and Image Sciences*, lays out a framework for integrating genomics, transcriptomics, radiology images, and electronic health records within a single modeling approach. Kolla argues that most existing deep learning research focuses on one data type and one task, making it difficult to understand broader biological mechanisms, as real disease processes rarely appear in just one kind of data. The paper notes that no true end-to-end multimodal foundation model has yet been built specifically for precision medicine. Kolla outlines an approach where each data modality is encoded separately and combined through a shared representation layer, using techniques including self-attention mechanisms and contrastive learning methods. A substantial portion of the paper addresses practical challenges of building data infrastructure, noting that obtaining large, high-quality, and well-curated datasets remains one of the hardest parts of any machine learning project.