Data-Centric Foundation Models in Computational Healthcare: A Survey

• URL: http://arxiv.org/abs/2401.02458v2
• Date: Mon, 07 Oct 2024 14:20:42 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: 21.53211505568379
• License:
• 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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