AIVO Journal — Sector Governance Series

AI assistants now influence how patients, clinicians, caregivers, and even regulators understand medicines, devices, indications, contraindications, risks, and clinical options. These systems answer questions long before individuals reach official channels. They often shape expectations about treatment pathways, benefit risk profiles, safety considerations, and comparative options.

Unlike other industries, healthcare and pharma operate within strict regulatory, scientific, and safety frameworks. When AI-generated information diverges from approved labeling or validated evidence, the misstatement does not simply misinform. It can become a clinical safety issue and a regulated compliance exposure.

This article provides a complete operating model for governing clinical visibility risk. It integrates medical, regulatory, pharmacovigilance, and technical controls.

1. Clinical misstatements as a distinct risk class

1.1 What counts as a clinical misstatement

A clinical misstatement is any incorrect AI-generated output about a medicine or device that contradicts approved labeling, validated evidence, or safety-critical information. Examples include errors in:

• Dosage or administration
• Contraindications
• Interactions
• Indications
• Clinical trial results
• Safety warnings
• Age-specific or pregnancy-specific guidance

These errors have potential clinical impact even if the organization did not produce or endorse them.

1.2 Why this differs from other industry risks

Commercial categories face reputational or competitive harm. Healthcare faces:

• Patient harm
• Regulatory breach
• Pharmacovigilance reporting obligations
• Product liability exposure

AI variability becomes a safety risk, not a usability concern.

2. Regulatory and legal foundations

Healthcare is governed by explicit statutory and guidance frameworks. AI visibility governance must align with these.

2.1 FDA

Relevant frameworks include:

• 21 CFR Part 202: Prescription Drug Advertising
• 21 CFR 201.57: Labeling content and format
• 21 CFR 314.80: Postmarketing reporting of adverse drug experiences
• FDA Guidance: Internet and Social Media Platforms with Character Space Limitations
• FDA Guidance: Presenting Risk Information in Prescription Drug and Medical Device Promotion

2.2 EMA and EU frameworks

Key sources include:

• Directive 2001/83/EC: Community code for medicinal products
• Regulation (EC) No 726/2004: Marketing authorisation procedures
• EU Clinical Trials Regulation 536/2014
• EU MDR and IVDR for medical devices

2.3 ICH

Critical guidelines include:

• ICH E2A: Clinical Safety Data Management
• ICH E2C(R2): Periodic Benefit Risk Evaluation Report
• ICH M4: Common Technical Document

2.4 Implications for AI misstatements

• Off-label claims may be interpreted as promotional exposure
• Incorrect risk information may conflict with approved labeling
• Misrepresentation of efficacy can be treated as misleading promotion
• Organizations must demonstrate reasonable steps to detect and mitigate unsafe information circulating in the market

Regulators assess overall market impact, not only corporate intent.

3. Ground truth governance for clinical information

Healthcare organizations cannot rely on informal or marketing-led definitions of truth. Ground truth must reflect regulatory controlled documents and evidence hierarchies.

3.1 Ground truth hierarchy

Tier 1: Regulatory approved documents

• SmPC, PI, PIL
• REMS materials
• Boxed warnings
• Dear Healthcare Professional Communications
• Contraindications and warnings

Tier 2: Core clinical evidence

• Pivotal trial results
• Summary of Clinical Efficacy and Safety (from submissions)

Tier 3: Authoritative guidelines

• NCCN, ADA, ESC, GOLD, EULAR, NICE, SIGN

Tier 4: Peer reviewed literature
Used for context, not for overriding approved labeling.

3.2 Update cadence

Ground truth must update in response to:

• Label revisions
• New safety findings
• New guideline releases
• Recalls and product withdrawals
• New therapeutic indications

3.3 Ownership

Ground truth must be co-owned by:

• Medical Affairs
• Regulatory Affairs
• Pharmacovigilance
• Compliance

Engineering uses ground truth, but Medical and Regulatory define it.

4. Failure modes unique to healthcare and pharma

AI assistants consistently exhibit failure patterns that are operationally and clinically significant.

4.1 Dosing and administration errors

Incorrect titration advice, invented dosing bands, or simplified administration steps.

4.2 Fabricated or missing contraindications

Missing contraindications can cause harm. Invented contraindications can deter necessary treatment.

4.3 Off-label claims

AI frequently suggests unapproved uses. Regulators expect companies to mitigate known off-label exposure.

4.4 Outdated clinical guidelines

Assistants often reference outdated guideline versions or ignore newly approved therapies.

4.5 Incorrect trial or efficacy statements

Invented trial data, fabricated cohort descriptions, or incorrect efficacy percentages.

4.6 Demographic misalignment

Particularly common for:

• Pregnancy
• Pediatrics
• Elderly
• Renal and hepatic impairment

These groups require strict adherence to labeling.

4.7 Rare disease conflation

AI merges different conditions with similar names, presenting incorrect treatment pathways.

4.8 Medical device misstatements

AI may misstate:

• Indications for use
• MRI safety conditions
• Implant compatibility
• Device class and regulatory approval status

5. Pharmacovigilance integration

AI misstatements intersect directly with pharmacovigilance systems.

5.1 Signal detection

Per ICH E2A, repeated misstatements about safety, interactions, or contraindications may constitute a signal requiring structured evaluation.

5.2 Case reporting

If a patient experiences harm after following AI-generated misinformation, the adverse event may require:

• Narrative inclusion of AI misstatement
• Causality assessment
• Reporting within required timelines

5.3 Periodic safety reports

Visibility findings may appear in:

• PSUR
• PBRER
• Development Safety Update Reports

5.4 Risk Management Plans

High-risk or high-variability products should include visibility monitoring as an RMP risk minimisation activity.

5.5 QPPV oversight

Qualified Person for Pharmacovigilance must review visibility findings at set intervals.

6. Designing clinical probe sets

Clinical probes must reflect how patients and clinicians seek information. They cannot be limited to brand queries.

6.1 Five core query categories

1. Brand-based queries
"What is [Drug X] used for?"

2. Indication-based queries
"Best treatment for type 2 diabetes with CKD."

3. Symptom-based layperson queries
"My asthma is getting worse at night. What should I take?"

4. Comorbidity and demographic scenarios
"Is this drug safe in pregnancy?"
"Adjustments for renal impairment."

5. Interaction queries
"Can I take [Drug X] with warfarin?"

6.2 Therapeutic area templates

Probes require tailored sets for:

• Oncology
• Diabetes
• Cardiovascular disease
• CNS disorders
• Infectious diseases
• Rare diseases
• Biologics and immunotherapies

6.3 Condition and symptom framing

Many errors surface only when queries are phrased in non-clinical language.

6.4 Global variation testing

AI often collapses jurisdictional differences. Probes must check:

• Region-specific approvals
• Region-specific contraindications
• Region-specific dosing rules

7. Thresholds and severity classification

Not all misstatements carry equal risk. Healthcare requires a structured severity model.

7.1 Severity classes

S4: Life-threatening
Examples include incorrect emergency dosing, pregnancy contraindications, or fabricated toxicity guidance.

S3: Serious
Incorrect contraindications, interaction errors, or dosing for chronic conditions.

S2: Moderate
Misleading benefit risk framing or incomplete safety profiles.

S1: Low
Terminology drift or minor descriptive inaccuracies.

7.2 Stability floors

• S4: 99.9 percent
• S3: 99.5 percent
• S2: 98 percent

7.3 Escalation timelines

• S4: immediate
• S3: within 24 hours
• S2: within 72 hours

This creates a medical-grade interpretation of visibility stability.

8. Multi-market complexity

AI assistants frequently present cross-market information without regard to jurisdiction. This creates new governance problems.

8.1 Region-specific indications and restrictions

Products approved in the US may not be approved in the EU, or may have different contraindications.

8.2 Divergent safety warnings

Black box warnings in one country may not exist in another.

8.3 AI conflating markets

Assistants frequently output:

• US indication to EU patients
• EU contraindication to US clinicians
• Outdated labeling to global audiences

Visibility governance must treat these patterns as structured risk.

9. Remediation strategy for clinical misstatements

Detection is not enough. Organizations need a clear action model.

9.1 Decision framework

When a clinical misstatement is detected, consider:

1. Severity classification
2. Whether the misstatement could lead to harm
3. Whether it could trigger regulatory scrutiny
4. Whether it could influence clinical practice behaviour

9.2 Remediation options

• Engage AI vendor through enterprise escalation
• Issue clarifying public statements
• Update official product websites for clarity
• Notify regulators where required
• Communicate with healthcare professionals when risk warrants
• Update probe sets and ground truth

9.3 Documentation

Every remediation step must produce an auditable record:

• Finding
• Severity classification
• Medical and regulatory assessment
• Decision and justification
• Corrective action
• Evidence pack

10. Integration with existing medical structures

10.1 Medical Information

MI teams handle unsolicited inquiries. They can inform probe design and update ground truth.

10.2 MLR (Medical, Legal, Regulatory) Review

Promotional review must include:

• Visibility drift assessment
• Clarity evaluation
• Risk of misinterpretation by AI systems

10.3 Internal AI models

If the organisation deploys RAG systems or internal copilots, these must pass visibility controls tuned to clinical constraints.

11. Clinical maturity roadmap (twelve months)

Phase 1 (Months 1 to 2)
Build clinical ground truth and define high-risk domains.

Phase 2 (Months 2 to 4)
Deploy foundational probes for approved indications.

Phase 3 (Months 4 to 6)
Expand to symptom-based, comorbidity, and demographic queries.

Phase 4 (Months 6 to 9)
Integrate with pharmacovigilance and regulatory review.

Phase 5 (Months 9 to 12)
Add global harmonisation and continuous monitoring.

Phase 6
Establish visibility metrics as part of routine safety governance.

Closing

AI-generated misstatements about medicines and devices are not simply errors in content retrieval. They represent a new category of clinical and regulatory risk that emerges outside formal communication channels, yet influences patient and clinician decisions in meaningful ways. Healthcare organizations must adopt visibility governance that reflects the safety, regulatory, and evidentiary obligations of the sector. Without these controls, organizations cannot demonstrate reasonable oversight of how their products are represented in environments that increasingly shape medical understanding.

If you want to review how these patterns appear for your organization, you can request a short evidence pack from audit@aivostandard.org. It summarises volatility, substitution, and reasoning drift under controlled test conditions.