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Toward Artificial Intelligence-driven Clinical Decision Support Tools in Rheumatology.
Ilana M Usiskin1, Maria I Danila2, Tianxi Cai3
1Division of Rheumatology, Inflammation, and Immunity, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
Rheumatic Diseases Clinics of North America
|July 6, 2026
Summary
Clinical decision support systems (CDSS) can improve rheumatology care. Newer artificial intelligence (AI)-driven CDSS show promise for complex rheumatic diseases, offering benefits and potential challenges.
Area of Science:
- Rheumatology
- Medical Informatics
- Artificial Intelligence
Background:
- Rheumatic conditions present diagnostic and treatment complexities.
- Current diagnostic methods for rheumatic diseases are largely clinical.
- Existing clinical decision support systems (CDSS) have limitations in rheumatology.
Purpose of the Study:
- To review existing studies on CDSS in rheumatology.
- To highlight the potential of AI-driven CDSS in rheumatology.
- To discuss the benefits and challenges of AI-CDSS implementation.
Main Methods:
- Literature review of CDSS applications in rheumatology.
- Analysis of knowledge-based versus machine-learning-based CDSS.
- Evaluation of AI's suitability for complex rheumatic diseases.
Main Results:
- CDSS can aid in differential diagnosis, treatment decisions, and outcome prediction.
- AI-driven (machine-learning) CDSS may outperform rule-based systems for rheumatic conditions.
- Existing studies show potential but require further investigation.
Conclusions:
- AI-driven CDSS offer significant potential to advance rheumatology practice.
- Careful consideration of benefits and pitfalls is necessary for AI-CDSS integration.
- Further research is needed to optimize AI-CDSS for rheumatic diseases.
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