Auditing Artificial Intelligence Reasoning in Healthcare: The DEEP SEE TM -MIRROR Dual-Layer Architecture for Structured AI Analysis and Meta-Cognitive Safety Oversight

Abstract

Artificial intelligence is increasingly integrated into clinical decision support, predictive monitoring, and diagnostic interpretation across modern healthcare systems. While these technologies offer substantial analytical capability, their expanding use raises important concerns regarding the transparency, reliability, and accountability of how AI systems derive clinical conclusions. Existing research on trustworthy artificial intelligence has largely focused on model performance, data quality, explainability, and governance. However, comparatively less attention has been directed toward systematic evaluation of the reasoning pathways through which AI systems generate those conclusions. This paper proposes a dual-layer architecture for structuring and auditing artificial intelligence reasoning in healthcare. In this framework, AI reasoning is defined as the sequence of inferential steps through which an AI system transforms clinical data into analytical conclusions, while the reasoning pathway refers to the traceable chain of intermediate inferences, evidence use, and decision logic supporting those conclusions. The first layer adapts the DEEP SEE TM framework into a structured reasoning protocol that guides AI systems through seven stages—Describe, Expose, Examine, Probe, Scan, Explore, and Elevate—to support systematic signal detection, evaluation of contributing factors, identification of hidden assumptions, and generation of interpretable clinical insights. The second layer introduces the MIRROR framework as a meta-cognitive audit protocol that evaluates reasoning integrity, defined as the extent to which a reasoning pathway is evidence-based, logically coherent, considers alternative explanations, and appropriately reflects uncertainty. Together, the DEEP SEE TM -MIRROR architecture provides a structured approach in which AI systems perform structured clinical analysis followed by systematic reasoning audit. The framework is particularly applicable to AI systems that provide auditable intermediate outputs or reasoning traces, including large language model-based and hybrid clinical decision-support systems, while partial application may be feasible in conventional predictive models with limited transparency. An illustrative proof-of-concept demonstrates how the architecture can transform AI-generated outputs into structured and auditable reasoning pathways, while proposed evaluation dimensions outline how reasoning integrity may be assessed in practice. By integrating principles from patient safety science, cognitive psychology, and trustworthy AI, the DEEP SEE TM -MIRROR architecture offers a practical approach for improving transparency, reliability, and bias awareness in AI-assisted clinical decision systems. More broadly, it highlights the importance of evaluating not only what artificial intelligence systems predict, but how they reason.