Explainable Systems Engineering: A Causal AI Approach to Audit-Ready Clinical Decision Support on the Cloud

Abstract

Clinical artificial intelligence systems require transparency beyond model-level explanations to achieve regulatory compliance and stakeholder trust. Explainable Systems Engineering addresses this gap by providing comprehensive visibility across entire enterprise infrastructures supporting AI deployments. The framework integrates causal reasoning methods with systems engineering principles to trace clinical decisions from raw data acquisition through final recommendations. Causal machine learning techniques enable practitioners to understand dependencies between system components and predict the impacts of configuration changes. Bayesian networks model uncertainty propagation throughout decision pipelines. Data provenance mechanisms track every transformation applied to clinical information. Implementation across multiple healthcare organizations demonstrates feasibility without significant performance overhead. Regulatory auditors confirm that system-level transparency satisfies documentation requirements for medical device approval processes. Clinicians report increased confidence when explanations include infrastructure context alongside algorithmic reasoning. The framework enables root cause analysis during system anomalies and supports proactive risk assessment before deployment changes. Fairness analyses reveal and help remediate disparities across patient populations. Automated documentation generation reduces compliance burden while maintaining audit trail completeness. The convergence of causal artificial intelligence with enterprise transparency creates foundations for responsible clinical decision support deployments.