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

Robert Gigiu

TL;DR

This paper tackles the challenge of embedding explainability within NHS clinical safety for AI by introducing the Explainability-Enabled Clinical Safety Framework (ECSF), which weaves XAI outputs into the DCB0129/0160 lifecycle without altering compliance pathways. It maps global and local explanations from traditional models and LLMs to safety artefacts (Hazard Log, Clinical Safety Case, Post-Market Surveillance), establishing five explainability checkpoints that support hazard identification, validation, clinical evaluation, risk control, and longitudinal monitoring. The ECSF aligns with Good Machine Learning Practice, the EU AI Act, and NHS assurance frameworks, enabling traceability, accountability, and ongoing governance across the AI lifecycle. Through an illustrative sepsis-warning scenario and discussion of limitations and implementation pathways, the paper demonstrates how explainability evidence can become a regulated safety function, guiding practical adoption and future regulation.

Abstract

Artificial intelligence (AI) is increasingly embedded in NHS workflows, but its probabilistic and adaptive behaviour conflicts with the deterministic assumptions underpinning existing clinical-safety standards. DCB0129 and DCB0160 provide strong governance for conventional software yet do not define how AI-specific transparency, interpretability, or model drift should be evidenced within Safety Cases, Hazard Logs, or post-market monitoring. This paper proposes an Explainability-Enabled Clinical Safety Framework (ECSF) that integrates explainability into the DCB0129/0160 lifecycle, enabling Clinical Safety Officers to use interpretability outputs as structured safety evidence without altering compliance pathways. A cross-regulatory synthesis mapped DCB clauses to principles from Good Machine Learning Practice, the NHS AI Assurance and T.E.S.T. frameworks, and the EU AI Act. The resulting matrix links regulatory clauses, principles, ECSF checkpoints, and suitable explainability outputs. ECSF introduces five checkpoints: global transparency for hazard identification, case-level interpretability for verification, clinician usability for evaluation, traceable decision pathways for risk control, and longitudinal interpretability monitoring for post-market surveillance. Techniques such as SHAP, LIME, Integrated Gradients, saliency mapping, and attention visualisation are mapped to corresponding DCB artefacts. ECSF reframes explainability as a core element of clinical-safety assurance, bridging deterministic risk governance with the probabilistic behaviour of AI and supporting alignment with GMLP, the EU AI Act, and NHS AI Assurance principles.

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

TL;DR

This paper tackles the challenge of embedding explainability within NHS clinical safety for AI by introducing the Explainability-Enabled Clinical Safety Framework (ECSF), which weaves XAI outputs into the DCB0129/0160 lifecycle without altering compliance pathways. It maps global and local explanations from traditional models and LLMs to safety artefacts (Hazard Log, Clinical Safety Case, Post-Market Surveillance), establishing five explainability checkpoints that support hazard identification, validation, clinical evaluation, risk control, and longitudinal monitoring. The ECSF aligns with Good Machine Learning Practice, the EU AI Act, and NHS assurance frameworks, enabling traceability, accountability, and ongoing governance across the AI lifecycle. Through an illustrative sepsis-warning scenario and discussion of limitations and implementation pathways, the paper demonstrates how explainability evidence can become a regulated safety function, guiding practical adoption and future regulation.

Abstract

Artificial intelligence (AI) is increasingly embedded in NHS workflows, but its probabilistic and adaptive behaviour conflicts with the deterministic assumptions underpinning existing clinical-safety standards. DCB0129 and DCB0160 provide strong governance for conventional software yet do not define how AI-specific transparency, interpretability, or model drift should be evidenced within Safety Cases, Hazard Logs, or post-market monitoring. This paper proposes an Explainability-Enabled Clinical Safety Framework (ECSF) that integrates explainability into the DCB0129/0160 lifecycle, enabling Clinical Safety Officers to use interpretability outputs as structured safety evidence without altering compliance pathways. A cross-regulatory synthesis mapped DCB clauses to principles from Good Machine Learning Practice, the NHS AI Assurance and T.E.S.T. frameworks, and the EU AI Act. The resulting matrix links regulatory clauses, principles, ECSF checkpoints, and suitable explainability outputs. ECSF introduces five checkpoints: global transparency for hazard identification, case-level interpretability for verification, clinician usability for evaluation, traceable decision pathways for risk control, and longitudinal interpretability monitoring for post-market surveillance. Techniques such as SHAP, LIME, Integrated Gradients, saliency mapping, and attention visualisation are mapped to corresponding DCB artefacts. ECSF reframes explainability as a core element of clinical-safety assurance, bridging deterministic risk governance with the probabilistic behaviour of AI and supporting alignment with GMLP, the EU AI Act, and NHS AI Assurance principles.

Paper Structure

This paper contains 21 sections, 5 figures.

Table of Contents

  1. Introduction
  2. Methods
  3. Framework Development Approach
  4. Analytical Perspectives
  5. Data Sources
  6. Results: The Explainability-Enabled Clinical Safety Framework (ECSF)
  7. Overview
  8. Traditional and Deep Learning Explainability
  9. Explainability for Large Language Models and Generative AI
  10. Integration into Assurance Activities and Artefacts
  11. Illustrative Application Scenario
  12. ECSF Alignment with Regulatory Principles and Key Outcomes
  13. Discussion
  14. Reframing Explainability as a Safety Enabler
  15. Strengthening Traceability, Validation, and Accountability
  16. ...and 6 more sections

Figures (5)

  • Figure 1: Deterministic vs AI-Enabled Clinical Safety Comparison. This figure contrasts the assurance logic of traditional deterministic software under DCB0129/0160 with the probabilistic behaviour of AI-enabled systems, illustrating why additional explainability and post-market monitoring are required.
  • Figure 2: Explainability-Enabled Clinical Safety Framework (ECSF) lifecycle mapping showing integration of XAI evidence across DCB0129/0160 artefacts.
  • Figure 3: Explainability Techniques by Model Type and Evidence Role. This figure summarises major explainability methods (SHAP, LIME, Grad-CAM, attention/rationale tracing, and counterfactual explanations) and their evidentiary relevance across model classes.
  • Figure 4: This figure provides an illustrative, non-exhaustive mapping of explainability outputs (e.g., SHAP, LIME, Grad-CAM, rationale tracing, and monitoring metrics) to the corresponding stages and artefacts of the DCB0129/0160 safety lifecycle.
  • Figure 5: Governance Alignment Matrix summarising conceptual correspondence between ECSF checkpoints, Good Machine Learning Practice (GMLP) principles, EU AI Act provisions (Articles 9–15, 61–62), and NHS AI Assurance Framework domains (T.E.S.T.). The table illustrates how ECSF checkpoints operationalise transparency, risk management, and post-market assurance across multiple governance frameworks.