The AIR·MS data platform for artificial intelligence in healthcare

This is the authors' abstract. We don't add key points for this paper.

Objective To present the Artificial Intelligence-Ready Mount Sinai (AIR·MS) platform-unified access to diverse clinical datasets from the Mount Sinai Health System (MSHS), along with computational infrastructure for AI-driven research and demonstrate its utility with 3 research projects. Materials and Methods AIR·MS integrates structured and unstructured data from multiple MSHS sources via the OMOP Common Data Model on an in-memory columnar database. Unstructured pathology and radiology data are integrated through metadata extracted from and linking the raw source data. Data access and analytics are supported from the HIPAA-compliant Azure cloud and the on-premises Minerva High-Performance Computing (HPC) environment. Results AIR·MS provides access to structured electronic health records, clinical notes, and metadata for pathology and radiology images, covering over 12M patients. The platform enables interactive cohort building and AI model training. Experimentation with complex cohort queries confirm a high system performance. Three use cases demonstrate, risk-factor discovery, and federated cardiovascular risk modeling. Discussion AIR·MS demonstrates how clinical data and infrastructure can be integrated to support large-scale AI-based research. The platform’s performance, scale, and cross-institutional design position it as a model for similar initiatives. Conclusion AIR·MS provides a scalable, secure, and collaborative platform for AI-enabled healthcare research on multimodal clinical data.