Reimagining Clinical Data Architecture for Personalized Care
DOI:
https://doi.org/10.5281/zenodo.21412910Keywords:
Precision Medicine, Clinical Data, Data Platforms, Patient Records, Data Integration, Genomic Data, Clinical Annotations, Data Reuse, Learning Systems, Clinical Analytics, Foundation Models, Transfer Learning, Model Training, Data Interoperability, Privacy Preservation, Population Data, Medical Imaging, AI Healthcare, Data Democratization, Scalable Systems.Abstract
AI—driven precision medicine requires access to large amounts of clinical data together with detailed clinical annotations. Clinical data platforms support this need by integrating patient records, diagnostic data, and clinical research data within a collect—once, use—many approach. Rich patient records combined with scalable analytics platforms enable the reuse of clinical data for novel analyses. Such platforms create new clinical learning loops, allowing the delivery of genomic—guided precision therapeutics to more patients.
State—of—the—art clinical data platforms support dynamic, integrated clinical records, simplify cross—modality data collection, and facilitate the clinical adoption of AI technologies. The democratization of clinical data, imaging, and genomic data into open foundations allows the global research community to build foundation models that can later be adapted for clinic—specific contexts or for other clinical functions. Such foundation models offer rich transfer learning opportunities that reduce the cost of model training while increasing performance and addressing clinical bias. Emerging architectures—modelling techniques and privacy—preserving technologies address data interoperability challenges, supporting the creation of true population datasets at scale and at equal cost for all communities and demographics.
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