Is Your AI System Generating
the Evidence It Needs to Prove Real-World Value?
8 questions. Instant score. Downloadable report. Evaluate your organization's validation practices, governance controls, and monitoring systems — mapped to the FDA–EMA Good AI Practice principles and the RIGOR™ Framework lifecycle.
On January 14, 2026, the FDA and EMA jointly released the Guiding Principles of Good AI Practice in Drug Development — ten principles covering the full AI lifecycle, from requirements definition through post-market monitoring. Though currently non-binding, legal analysts across the industry have reached a consistent conclusion: early alignment is strategically necessary. The question facing every organization deploying AI in a regulated environment is no longer philosophical. It is operational.
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8 questions across the RIGOR™ governance lifecycle. Instant score, domain breakdown, and tier-specific recommendations. No account required.
Is Your AI System Generating the Evidence It Needs?
8 questions mapped to FDA–EMA Good AI Practice principles. Score 2 for Yes · 1 for Partially · 0 for No · Maximum: 16 points.
✓ Free · No account needed · 2 minutes · Downloadable reportWhat the FDA–EMA Good AI Practice Principles Actually Require
The January 2026 FDA–EMA guidance describes a lifecycle model for AI governance: requirements defined before deployment, validation decisions made with accountability in mind, governance structures active during operation, and continuous monitoring after go-live. The RIGOR™ Framework operationalizes exactly this lifecycle — and each question in this assessment maps to a specific governance domain.
| FDA–EMA Principle | What It Requires | RIGOR™ Module |
|---|---|---|
| Human-centric design; patient safety primary | Define intended use, affected populations, and safeguards before deployment | Requirements |
| Risk-based validation proportional to context | Document risk thresholds and validation criteria specific to intended function | Requirements Implementation |
| Data governance, documentation, traceability | Maintain audit-ready records of data sources, model versions, and decision logic | Implementation |
| Accountability structures and human oversight | Assign named responsibility for AI system performance; define escalation paths | Governance |
| Regulatory, scientific, cybersecurity compliance | Verify regulatory alignment; document security controls and access governance | Governance |
| Validation and fit-for-use measures | Independent evidence of performance in the intended clinical context | Operational Proof |
| Transparent explanation of AI limitations | Plain-language documentation accessible to clinical and non-technical stakeholders | Operational Proof |
| Lifecycle management and continuous performance | Monitor for drift, bias, and degradation; define intervention triggers | Runtime Monitoring |
Source: FDA and EMA. Guiding Principles of Good AI Practice in Drug Development. January 14, 2026. RIGOR™ Framework, Health AI LLC.
The FDA's Good AI Practice Guidance Signals a Structural Shift — Here's What It Actually Requires
Olga Lavinda, PhD · Founder & CEO, Health AI LLC · March 2026
A new joint FDA–EMA framework defines what responsible AI governance looks like in practice. The challenge for most organizations is not philosophical — it is operational. Most AI systems are validated for regulators and then discovered to be commercially worthless. RIGOR™ closes that gap by defining how evidence is generated after deployment.
For years, "responsible AI" has been a slogan. The FDA and EMA have just started turning it into an operational requirement. On January 14, 2026, the two agencies jointly released the Guiding Principles of Good AI Practice in Drug Development — ten principles governing the safe, responsible, and transparent use of artificial intelligence across the full product lifecycle.
The document is currently non-binding. Its significance is structural. When the FDA and EMA — together representing the world's two largest pharmaceutical markets — publish aligned expectations and explicitly state those expectations will underpin future guidance in both jurisdictions, the practical effect on submission review, inspection readiness, and vendor due diligence is immediate. Legal analysts across the industry reached the same conclusion: early alignment is strategically necessary, not optional.
The Gap Is Operational, Not Philosophical
Most healthcare organizations don't resist the idea of responsible AI. The stated commitments to safety, transparency, and accountability are genuine. The problem is the distance between principle and practice — and the data on that distance is unambiguous.
A late-2025 survey of 182 hospital leaders found that 70 percent had experienced at least one AI pilot failure attributable to weak endpoints, workflow misalignment, or data gaps. Only 22 percent said they were confident they could produce a complete AI audit trail within thirty days for regulators or payers. Most deployed AI systems have no mechanism to answer the question "what happened after it was used?" — ever.
The pattern extends beyond hospital systems. The 2026 HealthEdge Annual Payer Report found that while nearly all health payers have deployed AI in some form, only 31 percent have fully defined governance models and controls in place. The implication is direct: organizations with post-deployment real-world evidence will bill under new 2026 CMS CPT codes for AI-enabled services. Organizations without it will not.
What the Guidance Operationally Demands
Translated from regulatory language into operational requirements, the ten FDA–EMA principles describe a lifecycle model: requirements defined before deployment, architectural and validation decisions made with accountability in mind, governance structures in place during operation, evidence of real-world performance, and continuous monitoring thereafter.
The practical implication is significant: an AI system cannot be governed the way a plug-in is managed. It requires defined requirements before selection, structured validation before deployment, accountability structures during operation, documented real-world performance evidence, and active monitoring for drift and degradation after go-live. Very few healthcare organizations currently have that lifecycle infrastructure in place.
A System Built for This Moment
The emerging regulatory consensus implicitly assumes organizations already possess a structured lifecycle model for AI governance. The data makes clear that most do not. The RIGOR™ Framework was built as that operational model — not as a response to this guidance, but as an independent answer to the same underlying problem that Good AI Practice now formally defines.
For organizations building with this kind of structured lifecycle model, the FDA–EMA guidance is confirmation. For organizations that have not, it defines precisely what needs to be built. The question is no longer whether healthcare AI will be held to rigorous validation standards. The question is whether you build the evidence architecture before or after a consequential failure makes the decision for you.
References
1. FDA and EMA. Guiding Principles of Good AI Practice in Drug Development. January 14, 2026. fda.gov
2. Black Book Research. Hospital AI Governance Survey (n=182, Oct–Nov 2025). Becker's Hospital Review, November 12, 2025.
3. HealthEdge. 2026 Annual Payer Report: Healthcare AI Trends 2026.
4. Hussein R, et al. Advancing healthcare AI governance through a comprehensive maturity model. npj Digital Medicine. 2026.
5. RIGOR™ Framework, Health AI LLC. healthai.com/rigor
About This Assessment and the RIGOR™ Framework
What is an AI deployment readiness assessment?+
An AI deployment readiness assessment evaluates whether an organization has the governance structures, validation practices, accountability controls, and operational monitoring in place to deploy AI systems responsibly in regulated environments. This assessment maps to the FDA–EMA Good AI Practice principles and the RIGOR™ Framework lifecycle.
How do I know if my organization is ready to deploy AI?+
Organizations ready to deploy AI responsibly can answer yes to eight structural questions: whether they have defined the context of use, documented validation requirements, named accountability structures, established risk thresholds, externally validated deployed tools, maintained audit trails, monitored for performance drift, and built detection systems for silent AI failure.
What does the FDA require for AI deployment in healthcare?+
The FDA and EMA's January 2026 Guiding Principles of Good AI Practice require organizations to define the specific context of use, establish risk-based validation, maintain documentation and audit trails, assign named accountability structures, and continuously monitor AI performance after deployment. These principles are currently non-binding but expected to underpin future binding guidance in both the US and EU.
What is the RIGOR™ Framework for AI validation?+
RIGOR™ is a clinical AI validation framework developed by Health AI LLC and created by Olga Lavinda, PhD, a validation scientist and NIH-funded molecular pharmacologist. It covers five lifecycle modules: Requirements, Implementation Architecture, Governance, Operational Proof, and Runtime Monitoring. The framework maps directly to FDA–EMA Good AI Practice principles and has been selected over Amazon, Microsoft, IBM, SAS, NTT Data, Dell, and Oracle in competitive evaluations.
Last updated: March 2026 · RIGOR™ Framework · Health AI LLC
RIGOR™ Framework · Health AI LLC
AI Deployment Readiness Assessment
Generated by the RIGOR™ AI Deployment Readiness Assessment · healthai.com
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