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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2401.02458 |
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| _version_ | 1866914515427262464 |
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| author | Zhang, Yunkun Gao, Jin Tan, Zheling Zhou, Lingfeng Ding, Kexin Zhou, Mu Zhang, Shaoting Wang, Dequan |
| author_facet | Zhang, Yunkun Gao, Jin Tan, Zheling Zhou, Lingfeng Ding, Kexin Zhou, Mu Zhang, Shaoting Wang, Dequan |
| contents | The advent of foundation models (FMs) as an emerging suite of AI techniques has struck a wave of opportunities in computational healthcare. The interactive nature of these models, guided by pre-training data and human instructions, has ignited a data-centric AI paradigm that emphasizes better data characterization, quality, and scale. In healthcare AI, obtaining and processing high-quality clinical data records has been a longstanding challenge, encompassing data quantity, annotation, patient privacy, and ethics. In this survey, we investigate a wide range of data-centric approaches in the FM era (from model pre-training to inference) towards improving the healthcare workflow. We discuss key perspectives in AI security, assessment, and alignment with human values. Finally, we offer a promising outlook on FM-based analytics to enhance patient outcomes and clinical workflows in the evolving landscape of healthcare and medicine. We provide an up-to-date list of healthcare-related foundation models and datasets at https://github.com/Yunkun-Zhang/Data-Centric-FM-Healthcare. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_02458 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Data-Centric Foundation Models in Computational Healthcare: A Survey Zhang, Yunkun Gao, Jin Tan, Zheling Zhou, Lingfeng Ding, Kexin Zhou, Mu Zhang, Shaoting Wang, Dequan Machine Learning Artificial Intelligence The advent of foundation models (FMs) as an emerging suite of AI techniques has struck a wave of opportunities in computational healthcare. The interactive nature of these models, guided by pre-training data and human instructions, has ignited a data-centric AI paradigm that emphasizes better data characterization, quality, and scale. In healthcare AI, obtaining and processing high-quality clinical data records has been a longstanding challenge, encompassing data quantity, annotation, patient privacy, and ethics. In this survey, we investigate a wide range of data-centric approaches in the FM era (from model pre-training to inference) towards improving the healthcare workflow. We discuss key perspectives in AI security, assessment, and alignment with human values. Finally, we offer a promising outlook on FM-based analytics to enhance patient outcomes and clinical workflows in the evolving landscape of healthcare and medicine. We provide an up-to-date list of healthcare-related foundation models and datasets at https://github.com/Yunkun-Zhang/Data-Centric-FM-Healthcare. |
| title | Data-Centric Foundation Models in Computational Healthcare: A Survey |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2401.02458 |