ArticleJAMIA open2025
The AIR·MS data platform for artificial intelligence in healthcare.
Article in JAMIA open, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
2 citing papers in PubMed.
- Genetically supported drug target prioritization for rare diseases.Genome medicine · 2026Article
- Automating clinical phenotyping using natural language processing.Communications medicine · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
21 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.