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

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 PRISMA-ScR-guided scoping review was conducted. Searches were performed primarily in PubMed (MEDLINE), with supplementary screening in Google Scholar; the final search was completed on 30 November 2025. Records were screened in two stages and eligible studies were charted according to CDSS type, data sources, analytical methods, validation strategies, explainability features, interoperability elements, and reported evidence of clinical integration. Findings were synthesized using a taxonomy-based thematic approach rather than through quantitative pooling.

The reviewed literature clustered into four main technological paradigms: information-retrieval systems, phenotype- and ontology-driven reasoning tools, data-driven predictive models based on EHRs and AI methods, and interoperable infrastructures such as federated learning and knowledge graphs. In addition, a separate group of studies addressed clinical evaluation and translation readiness across these paradigms. Across these areas, the field showed substantial methodological diversity, but evidence for external validation, workflow-level integration, and real-world clinical implementation remained limited. Interoperability, explainability, and governance were recurring challenges across paradigms.

Rare disease CDSS research is moving from isolated diagnostic tools toward broader, interconnected diagnostic ecosystems. Progress toward clinically actionable implementation will depend on standardized data representations, stronger cross-institutional validation, explainable outputs aligned with clinical workflows, and interoperable infrastructures supported by appropriate governance. This review provides a taxonomy and conceptual framework to support the translational development of rare disease diagnostic CDSS.