This chapter presents a thorough exploration of Transfer Learning, Foundation models, and Multimodal AI, with a specific focus on their use and the hurdles they present in Medical Device AI models. It begins by discussing the methods of Transfer Learning and their shortcomings. It then closely examines how to select and fine-tune foundation models, also addressing the difficulties associated with them, such as fine-tuning issues and the problem of catastrophic forgetting. A considerable part of the chapter is devoted to multimodal machine learning models, which are highly applicable to medical devices. It emphasizes recent progress and existing challenges in this area. The chapter wraps up by providing an overview of the latest developments in Multimodal Generative AI and Massively Multimodal Masked Modeling (4 M), accompanied by a concise illustration of Multimodal AI.