Real-World Validation

Sources and review notes

These links support definition-level research and do not establish the regulatory status, safety, or suitability of any product.

The IMDRF SaMD clinical-evaluation document describes valid clinical association, analytical validation, and clinical validation as an iterative process tied to intended use. FDA's Good Machine Learning Practice principles add representative data, human-AI team performance, clinically relevant testing, clear user information, and monitoring of deployed models across the total product life cycle. NIST's voluntary AI Risk Management Framework organizes testing, evaluation, verification, validation, monitoring, and risk response across an AI system's context and life cycle. These frameworks support intended-use-specific and post-deployment evaluation but do not create one universal real-world-validation method, certify a product, or prove that retrospective, pilot, vendor, or single-site results generalize across versions, populations, settings, users, or workflows; the device-focused sources may not apply to every healthcare AI tool. Buyers must predefine representative cases, comparator and ground truth, subgroup and site analysis, workflow outcomes, human overrides, integration failures, safety and operational incidents, acceptance thresholds, monitoring cadence, version changes, rollback criteria, and accountable review.

• Source 1: www.imdrf.org/documents/software-medical-device-samd-clinical-evaluation
• Source 2: www.fda.gov/medical-devices/software-medical-device-samd/good-machine-learning-practice-medical-device-development-guiding-principles
• Source 3: www.nist.gov/itl/ai-risk-management-framework