Healthcare Technology
HealthTech companies need to move fast and still ship regulated products. Cybroscape provides a fit-for-purpose validation and AI-governance stack tuned for HealthTech velocity — including SaMD validation and clinical documentation for the evidence dossier.
Healthcare technology — SaMD, digital health and clinical platforms — has to reconcile software release velocity with regulated change control. Where a product is a medical device it carries MDR or FDA obligations and IEC 62304 lifecycle requirements; where it processes patient data it carries privacy obligations too. The usual difficulty is validating at the pace the product ships.
Identify The Roadblocks Slowing Your Growth
These challenges compound: cycles slow, scaling gets harder, and inspection posture drifts.
Regulations We Cover
Every healthtech engagement is mapped to the regulations your inspectors will use.
tech
- FDA SaMD / AI-ML action plan
- HIPAA
- GDPR
- IEC 62304
- 01Product designALMDesign historyA clearly stated intended use
- 02Software buildCI/CDTest automationAn IEC 62304 software lifecycle
- 03Data and modelsData pipelinesModel registryTraining data and model versions under control
- 04Clinical evaluationClinical dataPerformance studiesEvidence the product performs as claimed
- 05In useMonitoringIncident reportingReal-world performance and data privacy
Services & Products For Healthcare Technology
- AI Validation
- AI Governance
- SaMD Validation
- Clinical Documentation
AI/ML validation for a SaMD manufacturer
A Class II SaMD manufacturer had an ML-based clinical decision-support module ready to ship, but no defensible AI assurance stack.
Read the case studySaMD Velocity: Moving Fast in a Regulated Space
Software as a Medical Device companies face a tension that is structurally different from traditional medical device manufacturers: SaMD is developed in continuous delivery cycles where new versions deploy weekly or monthly, but the regulatory framework for medical devices was designed around discrete product releases with formal submission and clearance cycles. The gap between development velocity and regulatory cadence is the defining challenge of SaMD product management.
FDA's Predetermined Change Control Plan (PCCP) framework, finalised in 2023, provides the regulatory mechanism for managing this tension. A PCCP allows a manufacturer to describe in advance the types of changes they anticipate making to the SaMD — including algorithm updates, performance improvements, and expanded indications — along with the testing and validation methodology they will apply before implementing each change, and the performance monitoring plan that confirms each change performs as expected post-deployment. With an approved PCCP, covered changes can be implemented without a new 510(k) submission, enabling continuous improvement without regulatory bottleneck.
IEC 62304 in Agile Development Environments
IEC 62304 was written assuming a waterfall software development model: complete requirements before design, complete design before implementation, complete implementation before testing. Modern SaMD is developed in Agile sprints where these boundaries are deliberately blurred. Reconciling IEC 62304's lifecycle documentation requirements with Agile sprint cadence is a practical challenge that most HealthTech companies encounter — and that most solve inadequately.
The solution is to map IEC 62304 deliverables to the Agile lifecycle artifacts that already exist: epics and user stories serve the function of software requirements; sprint review documentation and CI/CD test results serve the function of unit and integration testing records; product backlog management serves the function of software problem reporting. The IEC 62304 documentation is not produced by running a parallel waterfall process alongside the Agile development — it is produced by structuring the Agile artifacts to satisfy IEC 62304's content requirements. Our HealthTech validation practice has implemented this approach across multiple SaMD products, producing IEC 62304 compliant documentation as a natural output of the Agile process.
AI/ML Governance for Healthcare AI Products
Healthcare AI products — clinical decision support tools, diagnostic algorithms, patient risk stratification models — are subject to both FDA AI/ML guidance and the EU AI Act's high-risk AI system requirements for healthcare applications. Both frameworks share a common set of governance obligations: transparency about how the AI works and what it is and is not approved for, performance benchmarking and ongoing monitoring, human oversight for high-consequence decisions, and a documented change management process for algorithm updates.
GxP Copilot provides the governance infrastructure that healthcare AI products need. Its model card system documents the intended use, training data, performance benchmarks, and known limitations of each AI model in the product. Its drift monitoring capability tracks model performance against the benchmark as patient population characteristics evolve over time. Its change management workflow ensures that algorithm updates are assessed, tested, and documented before deployment. For HealthTech companies that need to demonstrate AI governance to FDA, payer Medical Technology Assessment bodies, or hospital procurement committees, GxP Copilot provides the audit-ready evidence package that supports those conversations.
Explore More For Regulated Life Sciences
Healthcare Technology validation without the backlog.
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