Data-Centric Foundation Models in Computational Healthcare: A Survey
- URL: http://arxiv.org/abs/2401.02458v1
- Date: Thu, 4 Jan 2024 08:00:32 GMT
- Title: Data-Centric Foundation Models in Computational Healthcare: A Survey
- Authors: Yunkun Zhang, Jin Gao, Zheling Tan, Lingfeng Zhou, Kexin Ding, Mu
Zhou, Shaoting Zhang, Dequan Wang
- Abstract summary: Foundation models (FMs) as an emerging suite of AI techniques have struck a wave of opportunities in computational healthcare.
We discuss key perspectives in AI security, assessment, and alignment with human values.
We offer a promising outlook of FM-based analytics to enhance the performance of patient outcome and clinical workflow.
- Score: 22.459507690070463
- License: http://creativecommons.org/licenses/by-nc-sa/4.0/
- Abstract: 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, ranging from 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 of FM-based analytics
to enhance the performance of patient outcome and clinical workflow 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 .
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