Essential Informatics Tools and Computing Infrastructure for Big Data to Advance Artificial Intelligence in Rheumatology.

Jeffrey R Curtis¹, Emily Holladay², Tapan Mehta³, John Osborne⁴

¹Division of Clinical Immunology & Rheumatology, University of Alabama at Birmingham, Birmingham, AL, USA. Electronic address: jrcurtis@uabmc.edu.

²Division of Clinical Immunology & Rheumatology, University of Alabama at Birmingham, Birmingham, AL, USA.

³Department of Family and Community Medicine, University of Alabama at Birmingham, Birmingham, AL, USA.

⁴Department of Medicine, University of Alabama at Birmingham, Birmingham, AL, USA.

PubMed DOI

Abstract

Rheumatic diseases are chronic, heterogeneous, and longitudinal, and assembling real-world evidence for effectiveness and safety for their study is best served by integrating diverse data types. This article describes the infrastructure required to support scalable, trustworthy artificial intelligence (AI) in rheumatology, emphasizing data acquisition, harmonization, linkage, privacy protection, a…

Create a free account to keep reading

Free members get 10 full research views every month across publications, clinical trials, FDA clearances, adverse events, and NIH grants. No credit card required.

Create Free Account Sign In

Want unlimited research access? See Pro plans

Data Accuracy Notice: Research intelligence on Health AI Central is aggregated from public sources (PubMed, ClinicalTrials.gov, FDA, NIH, CMS, and others) and refreshed nightly. Classifications and derived metrics are produced by automated methods described in our Methodology. We recommend verifying critical data points against the primary sources before making decisions.