PI-Backed Claims: Why LLMs Hallucinate About Pharma Products

TL;DR: Large language models hallucinate because they generate text based on patterns, not facts. For pharma, this means AI can invent efficacy claims, suggest off-label uses, or omit safety information - all with confidence. PI-backed verification checks every AI claim against your prescribing information, categorizing findings as Supported, Ambiguous, or Not-in-PI for compliance review.

Table of Contents

1. What is AI Hallucination?
2. Why Pharma Hallucinations Are Different
3. Types of Pharma-Specific Hallucinations
4. How PI-Backed Verification Works
5. The Supported / Ambiguous / Not-in-PI Framework
6. Real Examples of Hallucinated Claims
7. What to Do When You Find Inaccuracies
8. FAQ
9. Citations

What is AI Hallucination?

Hallucination occurs when an AI system generates information that sounds plausible but is factually incorrect. The AI isn't lying - it's doing what it was trained to do: predict the most likely next word based on patterns in its training data.

The problem: patterns don't equal facts. If an AI has seen many sentences about drug efficacy, it can generate new sentences about drug efficacy that follow the pattern but contain invented numbers, fake studies, or claims beyond approved labeling.

For general topics, hallucination is annoying. For healthcare, it's dangerous. A patient acting on hallucinated medical information could face real harm.

Why Pharma Hallucinations Are Different

Pharmaceutical products operate under strict regulatory frameworks. Every claim must be:

• Accurate: Supported by clinical evidence
• Balanced: Benefits presented with appropriate risks[1]
• On-label: Limited to approved indications
• Substantiated: Backed by adequate and well-controlled studies

AI systems don't know these rules. They generate text that sounds like pharma content but may violate any or all of these requirements.

The FDA's Office of Prescription Drug Promotion (OPDP) monitors advertising for misleading claims[2]. In 2023, OPDP issued enforcement letters for overstated efficacy and missing risk information. While those letters targeted company-created content, the same accuracy standards apply to information about your products - wherever it appears.

When 40 million people ask ChatGPT health questions daily[3], hallucinated claims about your drug reach massive audiences without your knowledge or control.

Types of Pharma-Specific Hallucinations

Our analysis has identified common hallucination patterns in pharma:

1. Efficacy Inflation

AI overstates treatment benefit:

• "Drug X reduces symptoms by 80%" when actual data shows 40%
• Comparative claims without head-to-head trials
• Cure claims for chronic conditions

2. Off-Label Suggestions

AI recommends unapproved uses:

• Pediatric dosing for adult-only medications
• Additional indications not in labeling
• Combination regimens not studied

3. Missing Safety Information

AI emphasizes benefits without balance:

• Black box warnings not mentioned
• Contraindications omitted
• Common side effects glossed over

4. Fabricated Studies

AI invents supporting evidence:

• "A 2024 study showed..." with no such study
• Attributed quotes from doctors who didn't say that
• Made-up clinical trial names

5. Outdated Information

AI cites superseded data:

• Old dosing before label updates
• Withdrawn indications still mentioned
• Safety concerns already addressed in new labeling

How PI-Backed Verification Works

PI-backed verification systematically checks AI-generated claims against your prescribing information (PI) - the FDA-approved source of truth for what can be said about your product.

The Process

1. Claim Extraction: Parse AI responses to identify discrete claims about your product (efficacy, dosing, safety, mechanism, indication)

2. PI Section Mapping: Match each claim to relevant PI sections (Indications and Usage, Dosage and Administration, Warnings, Clinical Studies, etc.)

3. Semantic Comparison: Compare claim meaning to PI text using embedding models that understand paraphrase and context

4. Classification: Categorize each claim as Supported, Ambiguous, or Not-in-PI

5. Evidence Documentation: Link each classification to specific PI sections (or lack thereof) for review

Why Semantic Matching Matters

AI doesn't quote your PI verbatim. It paraphrases, summarizes, and synthesizes. A claim like "reduces tumor size by 30%" should match "demonstrated 30% tumor reduction" even though the exact words differ.

Simple keyword matching misses these equivalences. Semantic matching catches them.

The Supported / Ambiguous / Not-in-PI Framework

Every claim falls into one of three categories:

Supported ✓

The claim directly matches content in your PI. It can be verified against approved labeling and defended if questioned.

Examples:

• Correct indication statement
• Accurate efficacy data from clinical studies
• Proper dosing information
• Safety statements matching Warnings section

Ambiguous ⚠️

The claim is partially supported or requires interpretation. The core fact may be accurate, but phrasing, context, or emphasis introduces uncertainty.

Examples:

• Comparative benefit without proper context
• Rounded or approximated data
• Benefit statement without balancing risks
• Mechanism claim that's simplified beyond accuracy

Not-in-PI ✗

The claim is not found in approved labeling. This is a compliance red flag requiring immediate review.

Examples:

• Off-label indications
• Fabricated efficacy numbers
• Invented studies or citations
• Safety claims contradicting known warnings

Real Examples of Hallucinated Claims

These are composite examples based on common patterns (not actual AI outputs for specific products):

Example 1: Efficacy Inflation

AI claimed: "Drug X reduces disease progression by 70% compared to placebo."

PI states: "Drug X demonstrated a 35% reduction in disease progression compared to placebo in the pivotal trial."

Classification: Not-in-PI (efficacy doubled)

Example 2: Off-Label Suggestion

AI claimed: "Drug X is often prescribed for pediatric patients starting at age 6."

PI states: "Safety and effectiveness in pediatric patients below 18 years of age have not been established."

Classification: Not-in-PI (off-label pediatric use)

Example 3: Missing Safety Balance

AI claimed: "Drug X is highly effective with minimal side effects."

PI states: "Black Box Warning: Drug X may cause serious cardiovascular events including myocardial infarction."

Classification: Not-in-PI (omitted Black Box Warning)

Example 4: Fabricated Study

AI claimed: "A 2025 real-world study at Johns Hopkins confirmed Drug X's superiority over Drug Y."

Reality: No such study exists.

Classification: Not-in-PI (fabricated evidence)

What to Do When You Find Inaccuracies

Immediate Actions

1. Document the finding: Capture exact AI response, provider, date, and query that triggered it

2. Classify severity: Not-in-PI claims with patient safety or compliance implications are highest priority

3. Route to Medical Affairs: Not-in-PI claims require medical/regulatory review before action

Medium-Term Actions

1. Identify source gaps: Why is AI generating this claim? Often it's citing low-quality sources or missing authoritative content

2. Publish corrective content: Create accessible, authoritative content that AI can cite instead

3. Update source footprint: Ensure medical education sites, your website, and partner publications have accurate, current information

Ongoing Actions

1. Retest after fixes: Run queries again to verify AI responses have improved

2. Monitor for drift: AI answers change; what's fixed today may drift tomorrow

3. Maintain audit trail: Document all findings and actions for compliance records

FAQ

Can we prevent AI from hallucinating about our products?

Not directly - you don't control AI training. But you can influence what AI says by strengthening your source footprint with accurate, authoritative content and monitoring to catch inaccuracies early.

Are we responsible for what AI says about us?

Legally, the liability landscape for AI-generated content is still evolving. Practically, patients and HCPs don't distinguish between company claims and AI claims - inaccuracies about your product affect your brand regardless of source.

How often do pharma hallucinations occur?

It varies by brand and therapeutic area. Brands with thin source footprints (few authoritative publications, limited medical education content) see more hallucinations because AI has less accurate material to draw from.

Does PI-backed verification catch everything?

It catches claims that can be compared to PI. It won't catch broader misinformation (e.g., false disease information) unless that misinformation specifically relates to your product's labeling.

Citations

[1] FDA OPDP The Brief Summary (Jan 2025 PDF): https://www.fda.gov/media/185040/download

[2] Covington - 2023 End-of-Year Summary of FDA Advertising and Promotion Enforcement Activity (Jul 22, 2024): https://www.cov.com/en/news-and-insights/insights/2024/07/2023-end-of-year-summary-of-fda-advertising-and-promotion-enforcement-activity

[3] Healthcare Dive - More than 40 million people ask ChatGPT healthcare questions every day (Jan 6, 2026): https://www.healthcaredive.com/news/40-million-use-chatgpt-health-questions-openai/808861/

For detailed methodology, see our PI-Backed Claim Defensibility page.

For term definitions, see our Glossary.