aceData-Centric Foundation Models in Computational Healthcare: A Survey
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…
Data-centric foundation models can improve healthcare workflows and enhance patient outcomes through better data characterization and scale from pre-training to inference.
Deploying foundation models in healthcare is limited by longstanding difficulties obtaining and processing high-quality clinical data due to quantity, annotation, privacy, and ethics constraints.
Effectiveness remains bounded by data quality, privacy, ethics, and unresolved issues of security, assessment, and value alignment.
Evidence
- Peer-reviewedAmerican Journal of Health and Medical Sciences2022-06-01
- Peer-reviewedMeta-Radiology2026-06-04
- Peer-reviewedElectronics2024-06-01
- Peer-reviewedEuropean Heart Journal Open2026-02-20
- Peer-reviewedACM Computing Surveys2026-03-28
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Truvace Impact Record TRV-2026-0345, v5: “Data-Centric Foundation Models in Computational Healthcare: A Survey.” Truvace, 2026-10-13. /record/TRV-2026-0345 (accessed at citation time). sha256 d5645b2531bb4157…
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