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• The paper proposes seven ontology-aware design patterns for building clinical AI pipelines resilient to ontological distortion.method 95%
• The patterns address data ingestion validation, low-frequency signal preservation, continuous drift monitoring, parallel representation maintenance, feedback loop interruption, terminology evolution management, and pluggable regulatory compliance.method 95%
• Three of the proposed patterns have partial precedent in existing systems, while the remaining four have not been formally described.result 90%
• The paper does not report empirical validation of the proposed patterns.limitation 95%
• The paper's limitations include the absence of runtime benchmarks for the proposed patterns.limitation 90%
• The proposed patterns are grounded in theoretical analysis of clinical data distortion mechanisms including documentary enactment, reification feedback loops, and terminology governance failures.technical 85%
• The paper provides a reference architecture for a primary care AI system illustrating composition of all seven patterns.method 90%
• Translating insights about clinical data distortion into implementable software architecture remains an open problem that this paper addresses.technical 80%

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