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The AIR·MS data platform for artificial intelligence in healthcare.

Pablo Guerrero1,2, Morten Ernebjerg1, Thomas Holst1

  • 1D4L Data4Life gGmbH, Potsdam, Brandenburg 14482, Germany.

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|November 21, 2025
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Summary

The Artificial Intelligence-Ready Mount Sinai (AIR·MS) platform unifies clinical data and computational resources, enabling large-scale AI research. It demonstrates high performance for cohort building and AI model training across diverse healthcare datasets.

Keywords:
artificial intelligencedata managementdata sciencedata warehousingelectronic health recordsinterdisciplinary researchmachine learning

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Area of Science:

  • Clinical Informatics
  • Artificial Intelligence in Healthcare
  • Data Science

Background:

  • Healthcare systems generate vast amounts of diverse clinical data.
  • Integrating disparate data sources and computational infrastructure is crucial for advanced AI research.
  • Existing platforms often lack the scale and flexibility required for complex AI model development.

Purpose of the Study:

  • To introduce the Artificial Intelligence-Ready Mount Sinai (AIR·MS) platform.
  • To showcase the integration of diverse clinical datasets and computational infrastructure.
  • To demonstrate the platform's utility through three AI research projects.

Main Methods:

  • AIR·MS integrates structured EHR data and unstructured pathology/radiology data using the OMOP Common Data Model.
  • Data is stored in an in-memory columnar database, with metadata linking raw source data.
  • HIPAA-compliant cloud and on-premises HPC environments support data access and analytics.

Main Results:

  • The platform provides access to over 12 million patient records, including EHRs, clinical notes, and imaging metadata.
  • AIR·MS supports interactive cohort building and AI model training with high system performance.
  • Three use cases demonstrated risk-factor discovery and federated cardiovascular risk modeling.

Conclusions:

  • AIR·MS successfully integrates clinical data and infrastructure for large-scale AI research.
  • The platform's scalability, security, and collaborative design serve as a model for future initiatives.
  • AIR·MS enables AI-driven healthcare research on multimodal clinical data.