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TRUVACE RECORD VERSION
record: TRV-2026-0345
version: 1
kind: certified
reason: Certified into the record
timestamp: 2026-07-20T08:53:29.860037Z
status: published
lens: trace
sector: health
headline: Data-Centric Foundation Models in Computational Healthcare: A Survey
dek: 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…
gain_title: Data-centric foundation models can improve healthcare workflows and enhance patient outcomes through better data characterization and scale from pre-training to inference.
problem_title: 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.
trace_subject: foundation models applied to computational healthcare workflows and patient outcomes
gain_reading: Data-centric foundation models can improve healthcare workflows and enhance patient outcomes through better data characterization and scale from pre-training to inference.
gain_evidence: enhance patient outcomes and clinical workflows
problem_reading: 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.
problem_evidence: obtaining and processing high-quality clinical data records has been a longstanding challenge, encompassing data quantity, annotation, patient privacy, and ethics
quick_read: Published March 28, 2026 in ACM Computing Surveys, this survey examines data-centric foundation models in computational healthcare, covering approaches from pre-training to inference aimed at improving clinical workflows. It highlights the shift toward data characterization, quality, and scale, and provides a public list of models and datasets.

The work matters because it connects AI technique advances to patient care delivery, while also surfacing persistent barriers around clinical data quantity, annotation, privacy, and ethics. Uncertainty remains about how security, evaluation, and alignment with human values will be addressed as these models move toward real-world use.
limitation: Effectiveness remains bounded by data quality, privacy, ethics, and unresolved issues of security, assessment, and value alignment.
tag: Automated dual reading
key_points: Survey covers data-centric approaches from model pre-training to inference for healthcare. | Identifies longstanding challenges in clinical data quantity, annotation, privacy, and ethics. | Discusses AI security, assessment, and alignment with human values in medical context. | Authors maintain a list of healthcare-related foundation models and datasets at a public GitHub repository.
rundown: The survey frames foundation models as interactive systems guided by pre-training data and human instructions, marking a data-centric paradigm focused on characterization, quality, and scale.

It notes the healthcare-specific bottleneck of clinical data records and points to ongoing work on security, assessment, and alignment, with an accompanying curated resource list linked in the text.
sources:
- peer_reviewed | ACM Computing Surveys | https://doi.org/10.1145/3800677 | 2026-03-28
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