Data-Centric Foundation Models in Computational Healthcare: A Survey
Yunkun Zhang Jin Gao Zheling Tan Lingfeng Zhou Kexin Ding Mu Zhou Shaoting Zhang Dequan Wang
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 .