Embedding Explainable AI in NHS Clinical Safety: The Explainability-Enabled Clinical Safety Framework (ECSF)

AI's unpredictable nature requires new safety standards: The paper identifies a fundamental conflict between AI systems' probabilistic and adaptive behavior and the deterministic assumptions built into existing NHS clinical safety standards (DCB0129 and DCB0160), meaning current governance frameworks are insufficient for AI-based medical devices.

Explainability can serve as safety evidence: The ECSF proposes that interpretability outputs from techniques like SHAP and LIME can be used as structured safety evidence within existing regulatory frameworks, allowing Clinical Safety Officers to understand why an AI made a decision without requiring entirely new compliance pathways.

Five critical checkpoints are needed across the AI lifecycle: The framework establishes five specific monitoring points—from initial hazard identification through post-market surveillance—to ensure AI systems remain safe and interpretable from development through real-world deployment in NHS workflows.

Regulatory alignment is achievable across multiple frameworks: The paper demonstrates that explainability requirements can be mapped across NHS standards, EU AI Act principles, and Good Machine Learning Practice guidelines, suggesting a unified approach to responsible AI governance is feasible.

Ongoing monitoring is essential for safety: The framework emphasizes "longitudinal interpretability monitoring" for post-market surveillance, recognizing that AI systems change over time and require continuous oversight to detect model drift and maintain clinical safety after deployment.