All terms
Explainability and Interpretability
Methods (SHAP, saliency maps, prototype explanations) that help users understand why a model made a given prediction.
Reviewed by Christian Espinosa, Founder, Blue Goat CyberLast reviewed May 5, 2026
Definition
Explainability provides post-hoc rationalizations of model decisions; interpretability is a property of inherently transparent models. Both feed clinician trust and inform Human Factors design. What the regulation says
Regulators distinguish between explainability and interpretability in artificial intelligence (AI) and machine learning (ML), emphasizing their importance for safety and effectiveness. The EU AI Act, for instance, requires high-risk AI systems to be designed with appropriate levels of interpretability, ensuring that their outputs can be understood by humans. Similarly, the FDA’s Good Machine Learning Practice (GMLP) principles advocate for transparency and understanding of ML model behavior, aligning with the need for both explainability and interpretability in MedTech.What this means in practice
EU AI Act high-risk requirements and FDA Good Machine Learning Practice both expect manufacturers to address explainability proportionate to clinical risk.Examples
- A diagnostic AI model for radiology provides an explanation, highlighting the specific regions in an image that led to its cancer detection, alongside the confidence score.
- An AI-powered insulin pump uses an inherently interpretable rules-based system, allowing healthcare providers to easily understand its dosing logic.
- During a post-market surveillance investigation, an explanation from an AI algorithm helps a manufacturer identify a data bias contributing to inaccurate predictions in a specific patient subgroup.
Common pitfalls
- •Confusing explainability as always providing full causal understanding of an AI model, rather than a justification for its output.
- •Assuming that an interpretable model eliminates the need for thorough validation and clinical risk management.
- •Failing to tailor the level of explainability or interpretability to the specific clinical application and user needs.
- •Overlooking the dynamic nature of AI models, where initial explanations may become irrelevant as the model evolves or is re-trained.
Frequently asked questions
Explainability helps clinicians understand why an AI model made a particular decision, fostering trust and enabling informed clinical judgment. It is crucial for incident investigation and continuous improvement.
Related terms
Shared paths + categorySoftware & AI
Algorithmic Bias and Fairness
Systematic differences in model performance across demographic or clinical subgroups.
Same category
Software & AI
Generative AI in Medical Devices
Devices that use LLMs or diffusion models to generate text, images, or recommendations within a clinical workflow.
AI/ML Devices Deep Dive · adjacent
Software & AI
Model Drift Monitoring
Ongoing surveillance of input data and model outputs to detect performance degradation post-deployment.
AI/ML Devices Deep Dive · adjacent
Software & AI
AI/ML-Enabled Medical Device
Medical device that uses artificial intelligence or machine learning to perform its intended use.
AI/ML Devices Deep Dive
Software & AI
Foundation Model (Healthcare)
Large pretrained model adaptable to many downstream clinical tasks via fine-tuning or prompting.
AI/ML Devices Deep Dive
Software & AI
Good Machine Learning Practice(GMLP)
Guiding principles for the development of AI/ML-enabled medical devices.
AI/ML Devices Deep Dive
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Primary references
3 sourcesLink health: 3 verified· last checked 2026-06-20
FDA·2MDCG·1
- 1FDA GMLPVerifiedFDAfda.gov
- 2MDCG Software GuidanceVerifiedMDCGhealth.ec.europa.eu
- 3FDA - Software as a Medical Device (SaMD)VerifiedFDAfda.gov
Inline markers like [1] jump to the matching reference above.