Foundation Model for Advancing Healthcare: Challenges, Opportunities, and Future Directions
<|reference_start|>Foundation Model for Advancing Healthcare: Challenges, Opportunities, and Future Directions: 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.<|reference_end|>
@article{he2024foundation,
    title={Foundation Model for Advancing Healthcare: Challenges, Opportunities,
  and Future Directions},
    author={Yuting He, Fuxiang Huang, Xinrui Jiang, Yuxiang Nie, Minghao Wang,
  Jiguang Wang, Hao Chen},
    journal={arXiv preprint arXiv:2404.03264},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.03264},
    primaryClass={cs.CY cs.AI}
}