This survey provides a comprehensive overview of foundation models (FMs) in healthcare. FMs are large-scale deep learning models trained on extensive datasets using self-supervised techniques. They serve as a base for various downstream tasks, including healthcare. FMs have been successfully adopted across multiple domains within healthcare, including natural language processing (NLP), computer vision, graph learning, biology, and omics. This survey focuses on the history, learning strategies, flagship models, applications, and challenges of FMs. It explores how FMs such as BERT and GPT families are reshaping various healthcare domains, including clinical large language models, medical image analysis, and omics data. The survey also provides a detailed taxonomy of healthcare applications facilitated by FMs, such as clinical NLP, medical computer vision, graph learning, and other biology-related tasks. Despite the promising opportunities FMs provide, they also have several associated challenges, which are explained in detail. The survey also outlines potential future directions to provide researchers and practitioners with insights into the potential and limitations of FMs in healthcare to advance their deployment and mitigate associated risks. The survey discusses the historical development of FMs, their learning algorithms, and key models such as BERT, GPT, CLIP, and others. It also covers the applications of FMs in healthcare, including clinical NLP, medical computer vision, healthcare graph learning, biology and omics, and other relevant areas. The survey highlights the major challenges associated with FMs and outlines future directions associated with FMs in healthcare. The survey also discusses the use of FMs in various domains, including medical image analysis, text-to-image generation, and omics data. The survey concludes with a discussion of the potential and limitations of FMs in healthcare.This survey provides a comprehensive overview of foundation models (FMs) in healthcare. FMs are large-scale deep learning models trained on extensive datasets using self-supervised techniques. They serve as a base for various downstream tasks, including healthcare. FMs have been successfully adopted across multiple domains within healthcare, including natural language processing (NLP), computer vision, graph learning, biology, and omics. This survey focuses on the history, learning strategies, flagship models, applications, and challenges of FMs. It explores how FMs such as BERT and GPT families are reshaping various healthcare domains, including clinical large language models, medical image analysis, and omics data. The survey also provides a detailed taxonomy of healthcare applications facilitated by FMs, such as clinical NLP, medical computer vision, graph learning, and other biology-related tasks. Despite the promising opportunities FMs provide, they also have several associated challenges, which are explained in detail. The survey also outlines potential future directions to provide researchers and practitioners with insights into the potential and limitations of FMs in healthcare to advance their deployment and mitigate associated risks. The survey discusses the historical development of FMs, their learning algorithms, and key models such as BERT, GPT, CLIP, and others. It also covers the applications of FMs in healthcare, including clinical NLP, medical computer vision, healthcare graph learning, biology and omics, and other relevant areas. The survey highlights the major challenges associated with FMs and outlines future directions associated with FMs in healthcare. The survey also discusses the use of FMs in various domains, including medical image analysis, text-to-image generation, and omics data. The survey concludes with a discussion of the potential and limitations of FMs in healthcare.