Ontology-Aware Design Patterns for Clinical AI Systems: Translating Reification Theory into Software Architecture

TL;DR

This paper introduces seven ontology-aware design patterns for building resilient clinical AI systems, translating reification theory into practical software architecture to address data and terminology distortions.

Contribution

It provides a novel set of design patterns grounded in reification theory, tailored for clinical AI pipelines to improve robustness against ontological distortions.

Findings

Patterns address data validation, drift monitoring, and terminology management.

Illustrated through a primary care AI system and diabetes risk prediction scenario.

Patterns have partial precedent; four are newly described.

Abstract

Clinical AI systems routinely train on health data structurally distorted by documentation workflows, billing incentives, and terminology fragmentation. Prior work has characterised the mechanisms of this distortion: the three-forces model of documentary enactment, the reification feedback loop through which AI may amplify coding artefacts, and terminology governance failures that allow semantic drift to accumulate. Yet translating these insights into implementable software architecture remains an open problem. This paper proposes seven ontology-aware design patterns in Gang-of-Four pattern language for building clinical AI pipelines resilient to ontological distortion. The patterns address data ingestion validation (Ontological Checkpoint), low-frequency signal preservation (Dormancy-Aware Pipeline), continuous drift monitoring (Drift Sentinel), parallel representation maintenance (Dual-Ontology Layer), feedback loop interruption (Reification Circuit Breaker), terminology evolution management (Terminology Version Gate), and pluggable regulatory compliance (Regulatory Compliance Adapter). Each pattern is specified with Problem, Forces, Solution, Consequences, Known Uses, and Related Patterns. We illustrate their composition in a reference architecture for a primary care AI system and provide a walkthrough tracing all seven patterns through a diabetes risk prediction scenario. This paper does not report empirical validation; it offers a design vocabulary grounded in theoretical analysis, subject to future evaluation in production systems. Three patterns have partial precedent in existing systems; the remaining four have not been formally described. Limitations include the absence of runtime benchmarks and restriction to the German and EU regulatory context.