Software as a Medical Device (SaMD) is software intended for one or more medical purposes that performs those purposes without being part of a hardware medical device. In practice, a product is assessed primarily by its intended purpose and claims: what the software says it does, who uses its output, and what healthcare decision or action the output supports.
That means a general-purpose phone app can be SaMD, while a technically sophisticated algorithm may fall outside device regulation if it is limited to administration, wellness, storage, transfer, display, or another non-medical function. Teams need to settle the intended use, risk, evidence, quality-system, and market pathway before treating a product as ready to launch.
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What SaMD means
The International Medical Device Regulators Forum (IMDRF) defines SaMD as software intended to be used for one or more medical purposes that perform these purposes without being part of a hardware medical device.
The U.S. Food and Drug Administration (FDA) reproduces this definition and describes SaMD as software that is itself a medical device.
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SaMD is different from software that is integral to a physical medical device, such as firmware that operates that device. It is also different from software used to manufacture, calibrate, service, or maintain a device. Those categories can have their own regulatory treatment, but they are not SaMD simply because they involve code.
For product teams, the practical question is not whether a model uses artificial intelligence, machine learning, signal processing, or a particular programming language. The question is what medical purpose the product is intended to perform and how its output is used.
- Medical purpose: The product may detect, diagnose, monitor, predict, treat, guide management of, or otherwise support care for a medical condition.
- Standalone operation: The software performs that purpose without being a component of a hardware medical device.
- Decision impact: The output may influence a healthcare decision or action, making the significance of the information and the clinical situation important to risk assessment.
Does your app qualify as SaMD?
Start with the product’s intended purpose, not its implementation. Write a plain-language statement that could appear in product documentation, labeling, a user interface, or a sales claim. Then examine every feature and claim against that statement.
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A useful statement identifies the medical condition or target, the user, the input data, the software output, and the action or decision the output is meant to support. Avoid describing the product only as a platform, dashboard, or algorithm. Those labels do not explain what the software is intended to do clinically.
For example, analyzes heart-rate signals to provide information to a clinician as an aid in diagnosing arrhythmia has a different regulatory meaning from displays heart-rate data for personal fitness tracking. The technical input may be similar, but the purpose and decision context are not.
2. Identify who receives and acts on the output
Record whether the output is intended for a patient, caregiver, clinician, laboratory professional, health-system operator, or another user. Then document whether the user merely views information, uses it to select or adjust management, or relies on it for diagnosis or treatment.
A statement such as for informational use only does not automatically remove a product from device regulation if other claims or the actual design direct users toward a medical decision. Conversely, a genuine administrative or wellness purpose may remain outside device regulation even when the product is used in a healthcare setting.
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3. Map each function, not just the product name
Large applications often contain a mixture of functions. A single product may include appointment scheduling, data storage, a clinician-facing display, an alert engine, and a diagnostic analysis module. Assess each function and each claim separately, then define which components are SaMD and which are not.
| Question | Evidence to review | Why it matters |
|---|---|---|
| What medical purpose is claimed? | Intended-use statement, labeling, website copy, onboarding text, contracts, and sales material | Claims establish what the software is intended to perform. |
| Who uses the output? | User roles, workflow diagrams, permissions, and training material | The same output can carry different risk for a patient, clinician, or administrator. |
| What decision or action follows? | Clinical workflow, alert logic, escalation rules, and user instructions | Decision significance helps determine the controls and evidence needed. |
| Is the software standalone? | System architecture, hardware dependencies, interfaces, and deployment model | Software integral to a hardware device is analyzed differently from standalone SaMD. |
Functions that may be outside or treated differently
Some administrative support, healthy-lifestyle functions, electronic-record functions, and data transfer, storage, or display features may be outside FDA device regulation. Certain limited clinical-decision-support functions may also be subject to enforcement discretion, depending on their exact function and the applicable policy.
These are not blanket exemptions. A feature that only stores or displays data can become materially different when it interprets the data, generates a medical recommendation, or drives an alert. The legal result depends on the precise function, claims, user, and jurisdiction. Keep the rationale for every in-scope and out-of-scope determination in the product record.
SaMD and related software: keep the boundaries clear
| Software category | Typical role | How to analyze it |
|---|---|---|
| Standalone SaMD | Performs a medical purpose on a general-purpose device or virtual environment | Assess intended purpose, clinical decision significance, healthcare situation, evidence, and market requirements. |
| Software integral to hardware | Operates or controls a physical medical device | Analyze it as software that is part of the hardware device rather than as standalone SaMD. |
| Manufacturing or maintenance software | Builds, calibrates, services, or maintains a device | Determine the applicable treatment for production or servicing software; do not label it SaMD merely because it supports medical-device operations. |
| Administrative or record-management software | Scheduling, billing, workflow, or record functions without medical interpretation | Check the exact function and claims; some may be outside device regulation. |
| Wellness software | General fitness, lifestyle, or health education | Assess whether the product makes a medical claim or supports a medical decision. |
| Transfer, storage, or display software | Moves, stores, or presents data without interpreting it | It may be outside device regulation, but interpretation, prioritization, or recommendations can change the analysis. |
Concrete examples of SaMD
Sleep-related breathing alert
FDA gives the example of software that uses a smart-device microphone to detect interrupted breathing during sleep and issue an alert. The medical purpose is not created by the microphone itself; it comes from using the audio signal to identify a condition-related event and notify the user.
Arrhythmia analysis
Another FDA example analyzes heart-rate signals or patterns for a clinician as an aid in diagnosing arrhythmia. The intended user, the diagnostic context, and the significance of the information all matter when determining the product’s risk and evidence burden.
Why the same technology can have different status
A signal-processing engine could support a medical diagnostic aid in one product and provide a non-medical fitness trend in another. The code may be similar, but the intended purpose, claims, target users, and downstream decisions differ. Teams should therefore freeze and control claims as carefully as they control source code.
How SaMD risk is categorized
IMDRF’s framework uses two dimensions together:
- Significance of the information: how the software output is used in relation to a healthcare decision. An output may inform a decision, help manage a condition, or play a more direct role in diagnosing or treating a patient.
- Healthcare situation or condition: the seriousness and urgency of the condition or situation in which the software is used.
Do not assign risk from the algorithm alone. A highly accurate model can still create substantial risk if users apply it in a serious situation, while a simple calculation may require strong controls when it directly informs a high-consequence decision.
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| Dimension | Questions for the team | Documentation |
|---|---|---|
| Information significance | Does the output merely provide background information, influence management, or support diagnosis or treatment? | Intended-use statement, workflow map, user instructions, and decision analysis. |
| Healthcare situation | Is the software used for a low-consequence situation, a serious chronic condition, or an urgent or life-threatening event? | Target condition definition, population, care setting, and escalation assumptions. |
| Combined category | What is the resulting IMDRF risk category for this intended use? | Risk rationale approved through the quality system and revisited after material changes. |
The category should scale controls, evidence, verification, validation, review, and monitoring. If the intended use expands from wellness coaching to clinical diagnosis, or from routine monitoring to urgent intervention, reassess the category rather than carrying forward the old analysis.
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- Write the intended purpose and claims. Use plain language and identify the condition, users, inputs, outputs, and supported action. Review user-interface text, marketing copy, contracts, and training material for claims that exceed the controlled statement.
- Map functions to the relevant definitions. Compare each function with the IMDRF and FDA SaMD concepts and with the legal device definition in every target market. Separate standalone medical functions from hardware control, administration, wellness, and display-only functions.
- Characterize risk. Document the healthcare situation and the significance of the information supplied to the decision. Record assumptions about users, population, care setting, and failure consequences.
- Choose controls before development accelerates. Establish a quality-system and lifecycle plan scaled to the product’s complexity and risk. Define who approves requirements, design changes, releases, incidents, and decommissioning.
- Build traceable requirements. Link intended-use claims to system requirements, architecture, risk controls, verification activities, validation activities, and release decisions. Traceability should show what was tested and why the result supports the claim.
- Plan the clinical evidence. Identify the clinical association, analytical validation, and clinical validation evidence needed for the intended users and context. Do this before finalizing the model, data pipeline, and user workflow.
- Confirm the market pathway. Consult the relevant regulator or qualified regulatory counsel for each market. Confirm classification, premarket expectations, quality requirements, postmarket duties, and any applicable enforcement policy before launch.
What a SaMD quality system must cover
IMDRF quality-management guidance describes lifecycle support processes from requirements through decommissioning. The system should be proportionate to the product’s complexity and should be updated as new information, risks, or milestones emerge. A startup does not need the same operational scale as a large platform, but it does need controlled decisions and objective records.
| Lifecycle stage | Controls and records to establish | Questions to answer |
|---|---|---|
| Planning and requirements | Controlled intended use, user needs, requirements, risk records, and regulatory assumptions | What must the software do, for whom, and under what conditions? |
| Architecture and design | Architecture description, interfaces, data flows, cybersecurity design, and risk controls | Where can failure, incorrect data, unauthorized access, or unsafe use occur? |
| Development | Version control, coding practices, review records, build configuration, and supplier controls where relevant | Can the released software be reproduced and traced to approved inputs? |
| Verification | Test plans, test results, anomaly records, and requirements traceability | Was each requirement implemented correctly? |
| Validation | Validation protocol and results using intended users, data, and context | Does the finished product achieve its intended purpose in real use conditions? |
| Deployment and release | Release criteria, change approval, configuration records, instructions, and training | What exact version is authorized, and what must users know? |
| Maintenance | Monitoring, complaint and incident handling, cybersecurity response, updates, and regression testing | How will the team detect and control new risks after launch? |
| Decommissioning | Retirement decision, user communication, data handling, support sunset, and dependency removal | How will use end safely without leaving users with misleading or inaccessible outputs? |
Clinical evaluation: the three evidence questions
IMDRF clinical-evaluation guidance organizes evidence around three connected questions. These are harmonized principles for jurisdictions to adopt; they are not regulations by themselves.
1. Is there a valid clinical association?
Clinical association asks whether the SaMD output is meaningfully related to the targeted clinical condition or physiological state. The team must explain why the measured input and computed output are relevant to the claimed purpose, using appropriate scientific and clinical support.
2. Does the software reliably produce its output?
Analytical validation evaluates whether the software processes inputs and generates outputs reliably. This includes the data pipeline, preprocessing, algorithm implementation, thresholds, interfaces, and behavior under expected operating conditions. A clinically plausible concept is not enough if the released software produces inconsistent or incorrect outputs.
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3. Does the output achieve the intended purpose in use?
Clinical validation examines performance with the intended users, target population, workflow, and context. It asks whether the output supports the claimed purpose, not merely whether the algorithm performs well on a development dataset. Define the population, setting, user actions, outcome measures, and acceptance criteria before running the validation.
| Evidence layer | Core question | Typical failure if omitted |
|---|---|---|
| Clinical association | Is the output clinically meaningful for the targeted condition? | The product measures something real but cannot support the claimed medical interpretation. |
| Analytical validation | Does the software consistently transform inputs into the specified output? | Implementation, data-quality, threshold, or integration errors undermine reliability. |
| Clinical validation | Does the output achieve its intended purpose for intended users and use conditions? | Strong laboratory performance fails to translate into safe or useful clinical workflow. |
Evidence should be proportionate to the intended purpose and risk. Preserve the rationale for the evidence plan, the data and protocols used, deviations, results, limitations, and the decision to release.
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Jurisdiction, classification, and market access
IMDRF documents are designed to harmonize principles, but an IMDRF category is not automatically a legal classification in every country. Each jurisdiction applies its own device definition, classification rules, quality expectations, evidence requirements, premarket route, postmarket obligations, and enforcement policies.
| Comparison axis | What to confirm locally |
|---|---|
| Intended purpose and claims | Which wording creates a medical-device claim, and which users and conditions are covered? |
| Healthcare situation | How does the market treat the seriousness, urgency, and care setting for the target condition? |
| Significance of information | Does the output inform, manage, diagnose, treat, or otherwise drive a decision? |
| Clinical evidence | What association, analytical, and clinical evidence is expected for the claimed use? |
| Quality system | Which lifecycle, design-control, cybersecurity, complaint, and change records are required? |
| Premarket route | Is a notification, authorization, registration, submission, or another review pathway required? |
| Postmarket obligations | What monitoring, incident reporting, corrective action, update, and retirement duties apply? |
| Enforcement policy | Are any limited functions subject to enforcement discretion, and under what exact conditions? |
The FDA’s reproduction of the IMDRF definition is useful for understanding the concept, but it does not replace a market-specific determination. Obtain a documented local assessment before relying on an out-of-scope conclusion or an enforcement policy.
Cybersecurity, updates, and change control
Software risk continues after the first release. Include cybersecurity and maintenance plans in the lifecycle system, with responsibilities for monitoring, vulnerability response, user communication, and release approval.
Classify changes before deployment. A cosmetic interface change may require different verification from a new data source, algorithm revision, threshold change, expanded population, or new clinical claim. For every change, assess whether the intended purpose, risk category, evidence, labeling, or market pathway changes. Run regression testing appropriate to the affected functions and retain the decision record.
Complaint and incident handling should connect field signals to corrective action. Define how users report unexpected outputs, how the team triages severity, how affected versions are identified, and how the resolution is verified. A decommissioning plan is also necessary: users need notice, support must end safely, and data and integrations must be handled according to the applicable obligations.
Common failure modes and how to correct them
Vague or drifting claims
Problem: Product, regulatory, and marketing teams use different descriptions of the software, causing the apparent scope to change over time.
Correction: Maintain one controlled intended-use statement and review every external claim against it before publication or release.
Treating a risk category as a universal legal classification
Problem: The team assumes that an IMDRF category automatically determines the legal route everywhere.
Correction: Use the category to structure risk analysis, then confirm the legal classification and pathway with the regulator or qualified counsel in each market.
Testing the model but not the product
Problem: The algorithm performs well in development, while data ingestion, preprocessing, interface behavior, alerts, or user interpretation are not validated.
Correction: Separate analytical validation from clinical validation and test the complete workflow with intended users, data, and context.
Skipping traceability
Problem: The team cannot show which test supports a requirement or which requirement supports a clinical claim.
Correction: Link claims, requirements, architecture, risks, tests, anomalies, and release decisions in a maintained traceability record.
Underestimating post-launch work
Problem: Updates, vulnerabilities, complaints, and new evidence are handled informally.
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Correction: Establish maintenance, cybersecurity, incident, change-control, and decommissioning procedures before launch, not after the first field issue.
SaMD launch-readiness checklist
- Controlled intended-purpose statement approved by the responsible team.
- Claims reviewed across the interface, labeling, website, contracts, and sales material.
- Function-by-function decision on SaMD scope, hardware integration, administration, wellness, and display-only behavior.
- Documented healthcare situation and significance of information supplied to the decision.
- Market-specific legal classification and pathway confirmed.
- Lifecycle quality system scaled to the product’s complexity and risk.
- Requirements, architecture, risks, controls, verification, and validation traceable.
- Clinical-association rationale documented.
- Analytical-validation plan and results completed for the released software.
- Clinical-validation plan and results address intended users, population, workflow, and context.
- Release, configuration, change-control, regression-test, and rollback procedures approved.
- Cybersecurity, maintenance, complaint, incident, and corrective-action processes active.
- User instructions and training explain limitations, operating conditions, and appropriate response to outputs.
- Postmarket monitoring and evidence-update responsibilities assigned.
- Decommissioning and user-communication plan approved.
What SaMD will require from your team
There is no single universal cost, timeline, or document set for SaMD. The workload depends on the intended purpose, healthcare situation, significance of the output, target users, data and software architecture, clinical evidence, selected markets, and the product’s classification in each jurisdiction.
The most reliable planning approach is to decide scope and risk before building a large feature set, keep claims under change control, and create the quality and evidence plan alongside development. SaMD is not defined by being an app or by using advanced technology; it is defined by standalone software performing a medical purpose, with controls and evidence scaled to the decisions that output may influence.
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