  Foundation model, which is pre-trained on broad data and is able to adapt to
a wide range of tasks, is advancing healthcare. It promotes the development of
healthcare artificial intelligence (AI) models, breaking the contradiction
between limited AI models and diverse healthcare practices. Much more
widespread healthcare scenarios will benefit from the development of a
healthcare foundation model (HFM), improving their advanced intelligent
healthcare services. Despite the impending widespread deployment of HFMs, there
is currently a lack of clear understanding about how they work in the
healthcare field, their current challenges, and where they are headed in the
future. To answer these questions, a comprehensive and deep survey of the
challenges, opportunities, and future directions of HFMs is presented in this
survey. It first conducted a comprehensive overview of the HFM including the
methods, data, and applications for a quick grasp of the current progress.
Then, it made an in-depth exploration of the challenges present in data,
algorithms, and computing infrastructures for constructing and widespread
application of foundation models in healthcare. This survey also identifies
emerging and promising directions in this field for future development. We
believe that this survey will enhance the community's comprehension of the
current progress of HFM and serve as a valuable source of guidance for future
development in this field. The latest HFM papers and related resources are
maintained on our website:
https://github.com/YutingHe-list/Awesome-Foundation-Models-for-Advancing-Healthcare.
