🤖 AI Summary
Clinical AI systems often suffer from ontological distortions in data due to fragmented medical documentation workflows, billing incentives, and inconsistent terminologies, and current architectures lack effective mechanisms to address these issues. This work proposes seven ontology-aware design patterns—four of which are novel—that operationalize reification theory into software architecture for the first time. These patterns encompass data validation, signal preservation, drift monitoring, dual-ontology representation, feedback disruption mitigation, terminology evolution, and regulatory compliance adaptation. By integrating ontology engineering, terminology governance, and regulatory requirements, the proposed pattern suite establishes an ontologically resilient reference architecture for clinical AI. The paper demonstrates the combined application of these patterns in a diabetes risk prediction scenario, providing a foundation for empirical evaluation.
📝 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.