Treat or Prevent
Software that drives a therapeutic action — closed-loop insulin dosing, automated ventilator adjustment, treatment protocol engines.
Four intended-use categories push software into SaMD territory. The line is your intended use statement — not how it's labeled. We work through this during discovery.
Software that drives a therapeutic action — closed-loop insulin dosing, automated ventilator adjustment, treatment protocol engines.
Software that identifies a disease or injury — AI image analysis for cancer or retinal disease, arrhythmia, seizure, and sepsis models.
Software that monitors clinical state for immediate action — continuous vital-sign analysis, real-time deterioration detection.
Risk stratification software whose output drives clinical decisions — treatment selection, intervention timing, or care escalation.
Six categories — each with its own regulatory pathway and clinical-validation requirements.
Most diagnostic AI and CDS falls Class II — 510(k) or De Novo, depending on risk and whether a predicate exists.
Information significance and condition seriousness decide it — most diagnostic AI and CDS lands at Class II.
Predicate exists → demonstrate substantial equivalence, the fastest pathway (3–12 months).
No predicate → FDA sets a new classification and clears at once (12–24 months).
A pre-sub with FDA's Digital Health Center of Excellence gets feedback on intended use and study design.
Clinical validation for SaMD is not software testing. It is a clinical study — designed with the rigor of a medical study, conducted with representative patient populations, and analyzed with statistical methods that provide meaningful evidence of clinical performance. Several principles apply broadly.
An algorithm validated only on academic-center data with high-quality imaging may not generalize to the community setting where it will be used. A sepsis model validated on one health system may not perform on a demographically different population. Study design accounts for the real-world deployment context.
Sensitivity and specificity for diagnostic software. Positive and negative predictive value at realistic disease prevalence. Area under the ROC curve where appropriate. Clinical outcomes where feasible — not just overall accuracy, which can be misleadingly high for rare conditions.
FDA increasingly expects — and clinical users reasonably demand — evidence of equitable performance across demographic and clinical subgroups. An algorithm at 95% sensitivity overall and 78% in elderly patients with comorbidities has a different clinical profile than the headline number suggests.
Post-market clinical data from real-world deployment is increasingly important to FDA's assessment — particularly for AI/ML products where real-world performance may differ from controlled-study performance. We build the real-world evidence infrastructure alongside the product.
Each card is a SaMD product we designed and cleared.
Talk to Our TeamSaMD carries a regulatory load spanning FDA device law, software lifecycle standards, clinical risk management, and global market access — scoped during discovery and built in from the start.
SaMD guidance, AI/ML action plan, and 510(k) / De Novo / PMA pathways.
Design-control requirements an FDA submission depends on.
Lifecycle process, risk management, usability, and cybersecurity.
PHI handling, encryption, access controls, and audited security.
EU MDR Article 22 SaMD provisions plus GDPR compliance.
Standards-based clinical data exchange and imaging, plus accessibility by design.
Organizations that discover SaMD requirements during development build them in. Those that discover them at launch redesign under pressure and delay market entry. The engagements that go well start with regulatory strategy. Thirty minutes. No pitch.
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The determining factor is intended use — what the software is designed to do with clinical data and what clinical decisions flow from its output. If your software analyzes patient-specific data to detect, diagnose, treat, or monitor a medical condition, it's likely SaMD. If it provides general reference information a clinician uses to independently make a decision, it may not be. This classification has a specific answer based on your intended use statement, and we work through it during discovery — before development begins.
510(k) requires a predicate — a legally marketed device with the same intended use and substantially equivalent technology. De Novo is for novel products where no predicate exists. 510(k) is faster. De Novo takes longer but creates a regulatory precedent that becomes your competitive moat — competitors filing 510(k)s in the same category will cite your clearance as their predicate.
It depends on risk classification and pathway. Class II 510(k) typically requires analytical validation and clinical validation data demonstrating the device performs as intended in a representative clinical population. De Novo may require more extensive evidence. The specific requirements are something we discuss with FDA in a pre-submission meeting before committing to a study design.
FDA's guidance on AI/ML-based SaMD introduces the Predetermined Change Control Plan — a document submitted with the original clearance that describes the types of algorithm changes that can be made post-clearance without a new submission. We design PCCP-eligible algorithm architectures and build the real-world performance monitoring infrastructure the PCCP requires.
510(k) review typically runs three to twelve months from submission. De Novo typically runs twelve to twenty-four months. These are FDA review timelines after submission — our development and submission preparation work is separate and typically runs six to eighteen months depending on scope and clinical validation complexity.
You do. Full IP transfer at project close — algorithm code, training-data infrastructure, clinical validation documentation, regulatory submission materials, everything.