--- title: "Explainability and Interpretability… | MedTech Terms" description: "Methods (SHAP, saliency maps, prototype explanations) that help users understand why a model made a given prediction." lang: en json-ld: | { "@context": "https://schema.org", "@graph": [ { "@type": "DefinedTerm", "@id": "https://medtechterms.com/terms/explainability#term", "name": "Explainability and Interpretability", "description": "Explainability provides post-hoc rationalizations of model decisions; interpretability is a property of inherently transparent models. 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[Software & AI](/terms?cat=Software%20%26%20AI) 6. / 7. Explainability and Interpretability [All terms](/terms) Software & AI [AI / ML in Devices](/ecosystems/ai-ml)[Software Lifecycle](/ecosystems/software-lifecycle) # 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 Cyber](/authors/christian-espinosa) Last 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](/terms/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](/terms/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](/terms/eu-ai-act) high-risk requirements and FDA [Good Machine Learning Practice](/terms/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 Why is explainability important in MedTech? 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. How does interpretability differ from explainability? Are both explainability and interpretability always required? ## Related terms Grouped by theme ### Editor's picks · Hand-selected related concepts [ Software & AI Algorithmic Bias and Fairness Systematic differences in model performance across demographic or clinical subgroups. ](/terms/ai-bias-fairness) ### AI/ML Devices Deep Dive · From this learning path [ Software & AI Generative AI in Medical Devices Devices that use LLMs or diffusion models to generate text, images, or recommendations within a clinical workflow. Adjacent lesson ](/terms/generative-ai-devices?from=ai-ml-devices-deep-dive)[ Software & AI Model Drift Monitoring Ongoing surveillance of input data and model outputs to detect performance degradation post-deployment. Adjacent lesson ](/terms/drift-monitoring?from=ai-ml-devices-deep-dive)[ Software & AI AI/ML-Enabled Medical Device Medical device that uses artificial intelligence or machine learning to perform its intended use. ](/terms/ai-ml-device?from=ai-ml-devices-deep-dive)[ Software & AI Foundation Model (Healthcare) Large pretrained model adaptable to many downstream clinical tasks via fine-tuning or prompting. ](/terms/foundation-model-health?from=ai-ml-devices-deep-dive) ### More in Software & AI · Same category [ Software & AI Good Machine Learning Practice(GMLP) Guiding principles for the development of AI/ML-enabled medical devices. ](/terms/good-machine-learning-practice)[ Software & AI Model Card Structured documentation of a machine learning model's intended use, performance, and limitations. ](/terms/model-card)[ Software & AI Predetermined Change Control Plan(PCCP) FDA mechanism to pre-authorize specific modifications to AI/ML-enabled devices. ](/terms/ai-ml-pccp) Cited by Where this term appears across MedTech Terms. Learning paths (1) - [AI/ML Devices Deep Dive](/paths/ai-ml-devices-deep-dive)Lesson 6 of 9 Ecosystems (2) - [AI / ML in Devices](/ecosystems/ai-ml) - [Software Lifecycle](/ecosystems/software-lifecycle) ## Primary references 3 sources Link health: 3 verified · last checked 2026-06-20 FDA· 2 MDCG· 1 1. [1 FDA GMLP Verified FDA · fda.gov ](https://www.fda.gov/medical-devices/software-medical-device-samd/good-machine-learning-practice-medical-device-development-guiding-principles) 2. [2 MDCG Software Guidance Verified MDCG · health.ec.europa.eu ](https://health.ec.europa.eu/medical-devices-sector/new-regulations/guidance-mdcg-endorsed-documents-and-other-guidance_en) 3. [3 FDA - Software as a Medical Device (SaMD) Verified FDA · fda.gov ](https://www.fda.gov/medical-devices/digital-health-center-excellence/software-medical-device-samd) Inline markers like \[1\] jump to the matching reference above. Sponsor note ### Building software-as-a-medical-device? We help SaMD and AI/ML device teams meet IEC 62304 and the FDA's evolving expectations for software cybersecurity. [Book a 30-minute discovery session](https://go.bluegoatcyber.com/meetings/blue-goat-cyber/discovery-session) - No obligation - Expert-led from minute one - NDA available on request MedTech Terms is a community resource sponsored by [Blue Goat Cyber](https://bluegoatcyber.com). Definitions are independent of any vendor. On this term Category Software & AI Sources 3 Updated 5/5/2026 [Compare with another term](/compare?a=explainability) Learn in 60 seconds Card Lesson Quiz Methods (SHAP, saliency maps, prototype explanations) that help users understand why a model made a given prediction. - · EU AI Act high-risk requirements and FDA Good Machine Learning Practice both expect manufacturers to address explainability proportionate to clinical risk. - · Both feed clinician trust and inform Human Factors design. Remember this Watch out: Confusing explainability as always providing full causal understanding of an AI model, rather than a justification for its output. Related terms - [Algorithmic Bias and Fairness ](/terms/ai-bias-fairness) You may also need Auto-suggested from Software & AI and shared keywords. - [Algorithm Change Protocol(ACP) ](/terms/algorithm-change-protocol) - [MLOps for Medical Devices(MLOps) ](/terms/mlops-medical-device) - [Human Factors Engineering(HFE) ](/terms/human-factors) - [Clinical Decision Support(CDS) ](/terms/cds) - [Foundation Model (Healthcare) ](/terms/foundation-model-health) - [Model Card ](/terms/model-card) [All Software & AI terms](/terms?cat=Software%20%26%20AI) From the Blue Goat network Related resources and services on this topic. - [ AI SaMD Insights How AI is changing SaMD - risk, regulation, and good machine-learning practice. ](https://ai-samd.com) - [ Med Device Cyber Podcast Conversations with medical device cybersecurity practitioners. ](https://mdcpodcast.com) MedTech Terms An authoritative, plain-language reference for the regulatory, quality, cybersecurity, and software terms that shape modern medical devices. 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