Architectural and translational perspectives on clinical decision support systems for rare disease diagnosis: a scoping review.

WHY IT MATTERS

If you're struggling to get a diagnosis for a rare disease, these computer decision support tools could help your doctors recognize your condition faster by analyzing your symptoms more systematically than traditional methods alone.

Doctors struggle to diagnose rare diseases because there are so many different symptoms and they don't see these diseases often. This article reviews computer programs designed to help doctors recognize and diagnose rare diseases faster and more accurately by organizing patient information and using artificial intelligence to spot patterns.

Architectural and translational perspectives on clinical decision support systems for rare disease diagnosis: a scoping review. Abstract: Rare diseases remain difficult to diagnose because of phenotypic heterogeneity, limited clinical familiarity, and fragmented health data infrastructures. Clinical decision support systems (CDSS) have emerged as promising tools to support earlier recognition and more consistent diagnostic reasoning. However, the literature spans diverse technological paradigms, making it difficult to understand how these systems collectively contribute to clinical decision-making and their translational implementation. This scoping review aimed to map diagnostic CDSS developed for rare disease diagnosis and to examine how data infrastructures, phenotype-driven reasoning frameworks, and artificial intelligence-based approaches contribute to clinical decision support and their translation into practice. A PRI Authors: Özçetin et al. Journal: International journal of medical informatics MeSH: Decision Support Systems, Clinical, Rare Diseases, Humans, Artificial Intelligence, Translational Research, Biomedical