paper-with-me

홈 › Papers

Foundation Model for Advancing Healthcare: Challenges, Opportunities, and Future Directions

2024-04-04 · Yuting He, Fuxiang Huang, Xinrui Jiang, Yuxiang Nie, Minghao Wang, Jiguang Wang, Hao Chen

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.

📄 PDF Abstract BibTeX arXiv:2404.03264

Code (1)

yutinghe-list/awesome-foundation-models-for-advancing-healthcare 공식 구현 pytorch

Tasks

Survey

Similar Papers 제목 키워드 기반

Open Challenges and Opportunities in Federated Foundation Models Towards Biomedical Healthcare

2024-05-10 · Xingyu Li, Lu Peng, Yuping Wang, Weihua Zhang

This survey explores the transformative impact of foundation models (FMs) in artificial intelligence, focusing on their integration with federated learning (FL) for advancing biomedical research. Foundation models such a…

DiagnosticFederated LearningSelf-Supervised LearningSurvey

Advancing Trustworthy AI in Healthcare Through Meta-Research: Results of an Interdisciplinary Design-Thinking Workshop

2026-02-27 · Valerie Bürger, Marlie Besouw, Jana Fehr, Riana Minocher 외 arxiv

Meta-research and Trustworthy AI (TAI) share common goals, namely improving evidence, robustness, and transparency, yet there is very little interplay between the two fields. To investigate the potential benefits of clos…

Generative AI for Healthcare: Fundamentals, Challenges, and Perspectives

2025-10-28 · Gang Chen, Changshuo Liu, Gene Anne Ooi, Marcus Tan 외 arxiv

Generative Artificial Intelligence (GenAI) is taking the world by storm. It promises transformative opportunities for advancing and disrupting existing practices, including healthcare. From large language models (LLMs) f…

Advancing Financial Engineering with Foundation Models: Progress, Applications, and Challenges

2025-07-07 · Liyuan Chen, Shuoling Liu, Jiangpeng Yan, Xiaoyu Wang 외 arxiv

The advent of foundation models (FMs), large-scale pre-trained models with strong generalization capabilities, has opened new frontiers for financial engineering. While general-purpose FMs such as GPT-4 and Gemini have d…

Multimodal Reasoning

Medical Multimodal Foundation Models in Clinical Diagnosis and Treatment: Applications, Challenges, and Future Directions

2024-12-03 · Kai Sun, Siyan Xue, Fuchun Sun, Haoran Sun 외

Recent advancements in deep learning have significantly revolutionized the field of clinical diagnosis and treatment, offering novel approaches to improve diagnostic precision and treatment efficacy across diverse clinic…

Diagnostic