A model card summarizes a model's purpose, training and evaluation data, performance metrics across relevant subgroups, ethical considerations, and known limitations to support transparent, responsible use. Model cards were proposed by Mitchell and colleagues in 2019 and are now used widely in AI documentation. In MedTech, they help meet transparency expectations such as FDA, Health Canada and MHRA's 2024 transparency principles for machine learning devices, and FDA's draft guidance suggests a model card format for labeling. They should be written for the intended user and kept in sync with the authorized device version.

Why it matters

Increasingly expected by regulators (e.g., FDA's transparency principles for ML-enabled devices) and procurement teams. Often combined with 'Indications-for-Use'-style cards for clinical AI.

Common pitfalls

•Failing to update model cards when models are retrained or modified, leading to outdated or inaccurate information, is a common pitfall.
•Overlooking the inclusion of performance metrics for all relevant subgroups, which can mask biases or disparities, is a mistake to avoid.
•Presenting overly technical information without clear, concise summaries for diverse audiences can hinder effective communication.
•Ignoring the iterative nature of AI development and not treating the model card as a living document can lead to compliance issues.

Sources

FDA Transparency Principles for ML Devices · FDA
FDA - Software as a Medical Device (SaMD) · FDA
FDA - AI/ML-Enabled Medical Devices · FDA

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Why compare MedTech terms side by side?

MedTech terminology is full of pairs that look interchangeable but carry very different regulatory, clinical, and commercial consequences. Picking the wrong framework, pathway, or standard early in a project can add months to a submission, invalidate clinical evidence, or trigger an audit finding. Side-by-side comparison is the fastest way to surface those differences before they become costly mistakes.

Each comparison on this page pulls from the same vendor-neutral, sourced definitions used throughout the MedTech Terms glossary. You see the one-line summary, the formal definition, why it matters in practice, common pitfalls, and the primary sources (FDA guidance, EU MDR/IVDR articles, ISO/IEC standards, MDCG documents, IMDRF principles) that back each entry. That makes the comparison defensible in regulatory strategy memos, design reviews, and submission narratives.

Common comparison patterns

Regulatory pathway choice - 510(k) vs PMA, 510(k) vs De Novo, Abbreviated vs Special 510(k).
Quality systems - QSR vs ISO 13485, QMSR vs QSR.
Software classification - SaMD vs SiMD, Locked vs adaptive AI.
EU frameworks - MDR vs IVDR, CE Mark vs UKCA.
Cybersecurity artifacts - SPDX vs CycloneDX SBOM, CVE vs CVSS.
Post-market actions - Recall vs field action, Removal vs correction.

How to use this tool

Pick term A and term B from the dropdowns, or click a preset above. The URL updates with both slugs so you can bookmark or share the exact comparison with a colleague, a notified body reviewer, or your regulatory consultant. Click Open full page on either side for the complete entry, including FAQs, related terms, and the full citation list. If you are not sure which term to start with, browse the Categories view or the A-Z index.

MedTech Terms is a vendor-neutral community resource sponsored by Blue Goat Cyber. Definitions are written for educational use and are not legal or regulatory advice.