[2401.02458] Data-Centric Foundation Models in Computational Healthcare: A Survey Skip to main content Search Submit Donate Log in Search arXiv Press Enter to search · Advanced search Computer Science > Machine Learning arXiv:2401.02458 (cs) [Submitted on 4 Jan 2024 ( v1 ), last revised 29 Apr 2026 (this version, v3)] 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 View a PDF of the paper titled Data-Centric Foundation Models in Computational Healthcare: A Survey, by Yunkun Zhang and 7 other authors View PDF HTML (experimental) 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, 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 this https URL . Comments: Published in ACM Computing Surveys Subjects: Machine Learning (cs.LG) ; Artificial Intelligence (cs.AI) Cite as: arXiv:2401.02458 [cs.LG] (or arXiv:2401.02458v3 [cs.LG] for this version) https://doi.org/10.48550/arXiv.2401.02458 Focus to learn more arXiv-issued DOI via DataCite Journal reference: ACM Comput. Surv. 58, 11, Article 287 (August 2026), 35 pages Related DOI : https://doi.org/10.1145/3800677 Focus to learn more DOI(s) linking to related resources Submission history From: Jin Gao [ view email ] [v1] Thu, 4 Jan 2024 08:00:32 UTC (846 KB) [v2] Mon, 7 Oct 2024 14:20:42 UTC (850 KB) [v3] Wed, 29 Apr 2026 07:43:10 UTC (883 KB) Full-text links: Access Paper: View a PDF of the paper titled Data-Centric Foundation Models in Computational Healthcare: A Survey, by Yunkun Zhang and 7 other authors View PDF HTML (experimental) TeX Source view license Current browse context: cs.LG < prev | next > new | recent | 2024-01 Change to browse by: cs cs.AI References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... Data provided by: Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer ( What is the Explorer? ) Connected Papers Toggle Connected Papers ( What is Connected Papers? ) Litmaps Toggle Litmaps ( What is Litmaps? ) scite.ai Toggle scite Smart Citations ( What are Smart Citations? ) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv ( What is alphaXiv? ) Links to Code Toggle CatalyzeX Code Finder for Papers ( What is CatalyzeX? ) DagsHub Toggle DagsHub ( What is DagsHub? ) GotitPub Toggle Gotit.pub ( What is GotitPub? ) Huggingface Toggle Hugging Face ( What is Huggingface? ) ScienceCast Toggle ScienceCast ( What is ScienceCast? ) Demos Demos Replicate Toggle Replicate ( What is Replicate? ) Spaces Toggle Hugging Face Spaces ( What is Spaces? ) Spaces Toggle TXYZ.AI ( What is TXYZ.AI? ) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower ( What are Influence Flowers? ) Core recommender toggle CORE Recommender ( What is CORE? ) IArxiv recommender toggle IArxiv Recommender ( What is IArxiv? ) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs . Which authors of this paper are endorsers? | Disable MathJax ( What is MathJax? ) We gratefully acknowledge support from our major funders , member institutions , , and all contributors. About · Help · Contact · Subscribe · Copyright · Privacy · Accessibility · Operational Status (opens in new tab) Major funding support from