AURELYN AI CLINICAL ACADEMY
Question 1 of 20
A diagnostic framework purpose-built for clinical research professionals — covering data governance, AI literacy, technology infrastructure, regulatory alignment, and leadership strategy.
Clinical data is among the most sensitive information categories regulated under HIPAA, GDPR, 21 CFR Part 11, and ICH E6(R3). Data governance and privacy maturity directly determines whether AI outputs are trustworthy, defensible to regulators, and safe for patients. This pillar is weighted 1.5× because a failure here isn't just an operational risk — it is a regulatory liability and patient safety concern.
An AI platform is only as effective as the team using it. Workforce literacy determines adoption speed, error detection rates, and the organization's ability to recognize when AI is wrong — a critical patient safety consideration in clinical research. Without it, even the best AI tools create new risks rather than reducing existing ones.
AI doesn't operate in isolation. Your existing eTMF, EDC, CTMS, and safety systems must be integration-ready for AI to deliver value rather than create more data fragmentation. Infrastructure maturity determines deployment speed, data quality, and whether your AI investments compound over time or create new silos.
Clinical AI operates in one of the world's most heavily regulated environments. Organizations that haven't mapped their AI use cases to ICH E6(R3), EU AI Act, 21 CFR Part 11, and NIST AI RMF risk deploying tools that create regulatory liability rather than reducing it. Compliance alignment is the difference between AI that passes inspection and AI that triggers a 483.
Technology implementations fail without executive sponsorship, clear strategy, and structured change management. In clinical research — where teams are risk-averse, regulatory pressure is constant, and change resistance is high — organizational will and strategic clarity are as important as the technology itself.
| LEVEL | SCORE RANGE | WHAT IT MEANS | STRATEGIC POSTURE |
|---|---|---|---|
🌱 AI Novice | 0 – 35% | Foundational gaps across data governance, AI literacy, and technology infrastructure. | Build the foundation. Immediate action on data governance and baseline training is critical. |
🧭 AI Explorer | 36 – 59% | Growing awareness with partial implementation. Some governance structures exist but AI-specific gaps remain. | Bridge the gaps. Structured training, formal governance, and regulatory alignment are the priorities. |
⚡ AI Practitioner | 60 – 79% | Solid foundations with active AI deployment. Strong governance and evolving workforce capability. | Accelerate. Close remaining gaps and scale AI-driven monitoring across the portfolio. |
🏆 AI Native | 80 – 100% | Comprehensive AI capability across all dimensions. Governance, literacy, infrastructure, compliance, and strategy all mature. | Lead. Leverage AI maturity as a competitive differentiator in bids and sponsor relationships. |
Data Governance & Privacy/Security receives a 1.5× weighting because failures in this dimension create the highest-consequence risk in regulated clinical research — including patient safety exposure, regulatory liability, and audit findings. A low score here requires immediate remediation regardless of scores in other pillars.
| LEVEL | RANGE | DESCRIPTION | YOUR STATUS |
|---|
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