Glossary

Verification and Validation (V&V)

The combined processes of confirming that design outputs meet design inputs (verification) and that the final device meets user needs and intended uses (validation), as required by FDA Quality System Regulations and IEC 62304.

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QUALITY ASSURANCE FRAMEWORK

What is Verification and Validation (V&V)?

Verification and Validation (V&V) are the combined, independent processes used to ensure a Software as a Medical Device (SaMD) is built correctly and fulfills its intended clinical purpose.

Verification confirms that design outputs meet specified design inputs, answering 'Did we build the software right?' It involves static testing, unit tests, and code reviews against IEC 62304 requirements to ensure the architecture correctly implements the documented specifications without defects.

Validation confirms that the final device meets user needs and intended uses in a clinical context, answering 'Did we build the right software?' It requires rigorous clinical evaluation against the Intended Use Statement, demonstrating diagnostic accuracy and safety through human factors testing and reader studies.

V&V COMPARISON

Verification vs. Validation: Key Distinctions

A structured comparison of the core attributes distinguishing verification activities from validation activities in the medical device software lifecycle.

Attribute	Verification	Validation
Core Question	Did we build the software right?	Did we build the right software?
Focus	Design outputs vs. design inputs	User needs and intended uses
Regulatory Reference	21 CFR 820.30(f)	21 CFR 820.30(g)
Primary Evidence	Test protocols, code reviews, unit tests	Clinical evaluation, usability studies
IEC 62304 Classification	Software system testing	Software system testing (user environment)
Typical Timing	Throughout development	Final production-equivalent device
Human Factors Integration
Clinical Context Required

VERIFICATION & VALIDATION

Frequently Asked Questions

Clarifying the distinct but interconnected processes that ensure a Software as a Medical Device (SaMD) is built correctly and meets its intended clinical purpose.

Verification confirms that design outputs meet design inputs—essentially asking, 'Did we build the software right?' It involves static testing, code reviews, unit testing, and traceability analysis against technical specifications like IEC 62304. Validation, conversely, confirms that the final device meets user needs and intended uses—asking, 'Did we build the right software?' This requires clinical evaluation, user acceptance testing, and demonstrating diagnostic accuracy in a simulated or actual use environment. While verification is an objective, internal engineering check, validation is a holistic, user-centric proof of clinical benefit and safety, often involving human factors engineering (HFE) to ensure the Intended Use Statement is fulfilled without use-related hazards.

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REGULATORY FRAMEWORK

Related Terms

Verification and Validation (V&V) is the backbone of regulatory submissions. These related concepts define the specific processes, standards, and documentation required to prove a SaMD product is safe and effective.

Design History File (DHF)

The Design History File is the master compilation of records proving a medical device was developed according to an approved design plan. It is the primary evidence repository for V&V activities.

Contains all verification protocols and test results
Houses validation study reports proving clinical efficacy
Demonstrates traceability from user needs to final design
Audited by the FDA during a QMS inspection

21 CFR 820.30

Regulatory Reference

IEC 62304

The international standard for medical device software lifecycle processes. It classifies software into three safety classes (A, B, C) based on the severity of potential harm, with Class C requiring the most rigorous V&V documentation.

Defines required software development planning artifacts
Mandates unit, integration, and system-level verification
Requires traceability between requirements, tests, and hazards
Harmonized with ISO 14971 for risk management integration

Analytical Validation

The controlled laboratory assessment of a diagnostic algorithm's technical performance characteristics. This is the verification arm of V&V for AI-based diagnostics.

Measures precision, accuracy, and robustness to input variation
Evaluates performance across diverse sample types and conditions
Establishes the algorithm's limit of detection and linear range
Generates the statistical evidence required for a 510(k) submission

Clinical Evaluation

The systematic process of generating and assessing clinical data to confirm a device performs as intended in its target population. This constitutes the validation phase of V&V.

Includes retrospective reader studies comparing AI-assisted vs. unaided diagnosis
May require prospective clinical trials for novel indications
Demonstrates diagnostic accuracy (sensitivity/specificity) in real-world use
Continuously updated through Post-Market Surveillance (PMS)

Substantial Equivalence (SE)

The core regulatory concept for 510(k) clearance, where a manufacturer demonstrates their device is as safe and effective as a legally marketed predicate device. V&V evidence is structured to prove this equivalence.

Verification tests compare output performance to the predicate
Validation studies must use comparable clinical endpoints
Any technological differences require additional analytical justification
A failed SE determination may necessitate a De Novo pathway

Predetermined Change Control Plan (PCCP)

An FDA-authorized plan detailing pre-specified modifications a manufacturer can make to an ML-enabled device without a new submission. It defines the V&V required for each change type.

Describes the SaLPM (Software as a Learning Performance Model) protocol
Pre-defines verification tests for model retraining and input shifts
Establishes validation thresholds that trigger regulatory review
Critical for maintaining clearance of adaptive algorithms

About the author

Prasad Kumkar

CEO & MD, Inference Systems

Prasad Kumkar is the CEO & MD of Inference Systems and writes about AI systems architecture, LLM infrastructure, model serving, evaluation, and production deployment. Over 5+ years, he has worked across computer vision models, L5 autonomous vehicle systems, and LLM research, with a focus on taking complex AI ideas into real-world engineering systems.

His work and writing cover AI systems, large language models, AI agents, multimodal systems, autonomous systems, inference optimization, RAG, evaluation, and production AI engineering.

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