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Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System

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

Jeffrey R Curtis¹, Emily Holladay¹, Tapan Mehta²

¹Division of Clinical Immunology & Rheumatology, University of Alabama at Birmingham, Birmingham, AL, USA.

Rheumatic Diseases Clinics of North America

|July 6, 2026

Summary

Building trustworthy artificial intelligence (AI) in rheumatology requires robust infrastructure for data integration, privacy, and computational environments. Investment in shared data ecosystems and governance is crucial for advancing AI in rheumatic disease research.

Keywords:

Artificial intelligence Big data Large language models Machine learning Ontologies

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Area of Science:

Rheumatology
Artificial Intelligence
Health Informatics

Background:

Rheumatic diseases are chronic and require diverse data for effective study.
Assembling real-world evidence for treatment effectiveness and safety is challenging.

Purpose of the Study:

To describe the necessary infrastructure for scalable and trustworthy artificial intelligence (AI) in rheumatology.
To outline computing infrastructure considerations for AI in rheumatology.

Main Methods:

Emphasis on data acquisition, harmonization, linkage, and privacy protection.
Discussion of hybrid on-premises and cloud computing architectures.
Consideration of governance models for AI development.

Main Results:

Scalable, trustworthy AI in rheumatology necessitates integrated data and secure computational environments.
Hybrid cloud architectures are relevant for rheumatology computing needs.
Shared infrastructure and longitudinal data ecosystems are vital.

Conclusions:

Sustained progress in AI for rheumatology depends on investment in shared infrastructure.
Effective governance models are needed to balance innovation, privacy, and clinical value.
Longitudinal data ecosystems are essential for advancing AI in rheumatic diseases.