AI Boundaries & Intended Use

Transparency in how AI is used on this platform

Last reviewed: March 2026  |  Aligned with FDA GMLP, EU AI Act, 21 CFR Part 11

Intended Use Statement

VirtualBackroom.ai is a regulatory compliance coaching and readiness assessment tool for medical device quality professionals.

Classification

This platform is an advisory and educational tool. It is not a Quality Management System (QMS), document management system, electronic quality management system (eQMS), or regulatory submission tool. It does not author, approve, or finalize any quality system documents.

The platform is designed to help quality and regulatory professionals:
  • Prepare for regulatory audits and inspections
  • Assess organizational readiness against regulatory requirements
  • Understand and cross-reference regulatory standards
  • Practice responding to audit scenarios in a safe environment
  • Identify potential compliance gaps for further investigation
  • Stay informed about FDA enforcement trends and regulatory updates

What AI on This Platform Cannot Do

The following are explicit boundaries on AI functionality within VirtualBackroom.ai. These boundaries exist to ensure that human professionals retain full authority over quality system decisions, consistent with FDA GMLP Principle #7 (Focus on the performance of the Human-AI Team) and EU AI Act Article 14 (Human Oversight).

Quality Decisions
  • Cannot make regulatory compliance decisions on behalf of your organization
  • Cannot close, approve, or sign off on CAPAs, deviations, nonconformances, or audit findings
  • Cannot determine batch disposition, product release, or field action decisions
  • Cannot serve as the basis for regulatory submissions without independent qualified human review
Document Authority
  • Cannot author documents for regulatory submission
  • Cannot replace formal internal audits, management reviews, or notified body assessments
  • Cannot provide legal advice or serve as legal counsel
  • Cannot replace qualified human review of any quality system decision
Data Handling
  • Cannot access, store, or process Protected Health Information (PHI) or patient data
  • Cannot guarantee accuracy of AI-generated regulatory citations without human verification
  • Cannot serve as a system of record for regulated quality data
Risk Decisions
  • Cannot perform risk assessments that satisfy ISO 14971 requirements without qualified human oversight
  • Cannot replace the independent judgment of qualified regulatory affairs, quality, or clinical professionals
  • Cannot establish or modify risk acceptability criteria for your organization

What AI on This Platform Is Designed to Do

AI features on this platform are designed to support — never replace — qualified human professionals. All AI outputs are preliminary and require independent verification.

Coaching & Training
  • Coach and prepare teams for regulatory audits and inspections
  • Simulate audit scenarios for educational purposes
  • Provide practice environments for audit response skills
Readiness Assessment
  • Assess organizational readiness against regulatory requirements
  • Identify potential compliance gaps for further investigation by qualified personnel
  • Generate preliminary analysis for human review and validation
Reference & Cross-Referencing
  • Provide regulatory reference material and standard cross-referencing
  • Map citations across FDA QMSR, ISO 13485, EU MDR/IVDR, and legacy QSR
  • Surface relevant FDA enforcement data and warning letter trends
Multi-Perspective Analysis
  • Provide multiple AI perspectives via Council Mode for broader consideration
  • Calculate confidence and consensus scores to inform human judgment
  • Flag areas where AI models disagree for closer human examination

How to Review AI Outputs

AI outputs on this platform include quality indicators (confidence scores, hallucination risk, citation verification) to help you evaluate reliability. Use the following checklist when reviewing any AI-generated content, aligned with FDA GMLP Principle #7 (Human-AI Team Performance).

The "human in the loop" must function as a control, not merely a step in a process. Your review should involve independent judgment, not just confirmation of what AI produced.
AI Output Review Checklist
Citation Accuracy
  • Does the AI cite specific regulatory clauses or standards?
  • Are the cited clauses correct and current?
  • Have you verified citations against the primary regulatory text?
  • Are cross-references between standards accurate (e.g., QSR to QMSR mapping)?
Confidence Assessment
  • What is the confidence score? Does it warrant reliance?
  • Is there a high hallucination risk flag? If so, treat with extra scrutiny
  • In Council Mode, do the AI models agree? Low consensus warrants further investigation
  • Does the response contain hedging language ("probably," "may," "I believe")?
Completeness & Relevance
  • Does the recommendation align with your organization's specific risk profile?
  • Are there gaps or areas the AI did not address?
  • Does the analysis account for your device classification and regulatory pathway?
  • Are there organizational or contextual factors the AI could not know?
Independent Judgment
  • Would a different qualified reviewer reach the same conclusion?
  • Have you considered what the AI might have missed?
  • Are you confirming with independent evidence, or just accepting the AI output?
  • Is this output being used as input for further human analysis, or as a final answer?
Understanding Quality Indicators
Indicator What It Means How to Respond
Confidence: High (>80%) AI's citations were verified and response is well-grounded in regulatory text Still requires human review, but higher reliability indicator
Confidence: Medium (50-80%) Some citations verified, but response may include interpretive content Cross-reference key claims against primary regulatory sources
Confidence: Low (<50%) Limited citation support; higher likelihood of interpretive or speculative content Do not rely without thorough independent verification by a qualified professional
Hallucination Risk: Elevated Response contains language patterns associated with uncertainty or fabrication Treat as preliminary only; verify all factual claims independently
Council Consensus: High Multiple AI models agreed on the analysis and citations Greater consistency, but consensus does not equal correctness
Council Consensus: Low AI models diverged significantly in their analysis Topic likely requires expert human judgment; AI perspectives are starting points only

Regulatory Alignment

VirtualBackroom.ai's AI governance framework is aligned with the following regulatory guidance and standards:

FDA Good Machine Learning Practice (GMLP)
  • Principle #2: Good Software Engineering and Security Practices are followed
  • Principle #7: Focus on the performance of the Human-AI Team — human oversight is a control, not a formality
  • Principle #9: Users are provided clear, essential information about AI system capabilities and limitations
  • Principle #10: Deployed models are monitored for performance and re-evaluated as needed
EU AI Act
  • Article 13 (Transparency): Users understand they are interacting with AI and what its limitations are
  • Article 14 (Human Oversight): Users can effectively oversee AI outputs, flag errors, and override recommendations
  • Article 9 (Risk Management): AI outputs are accompanied by quality indicators to support informed decision-making
21 CFR Part 11 / EU Annex 11
  • §11.10(a): System validation — AI features validated for intended use
  • §11.10(e): Authority checks — role-based access and approval controls
  • §11.10(k)(2): Audit trail accountability — all AI interactions logged with user attribution
  • ALCOA+ principles applied to all AI audit records
ISO 13485:2016 & ICH Q9
  • Clause 4.2.5: Document control principles applied to AI boundary documentation
  • Clause 8.2.2: Feedback mechanisms for AI output quality (complaint handling principles)
  • ICH Q9: Risk-based approach to evaluating AI outputs — higher risk topics receive more scrutiny
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