Hallucinations Aren’t Random: Understanding Model Confidence in AI Medical Writing

AI hallucinations are often described as unpredictable failures—or as evidence that generative AI can’t be trusted in regulated environments. 

That interpretation is understandable, but incomplete. 

In reality, hallucinations occur because large language models generate text based on probability, not verification. They are a predictable result of how AI systems express confidence when certainty is unavailable. Once that’s understood, hallucinations become easier to anticipate—and manage. 

What an AI Hallucination Actually Is 

A large language model predicts the most likely next token based on patterns in data. It doesn’t verify facts, assess evidence quality, or decide when silence is appropriate. 

When context is strong, predictions align closely with known information. When context is weak, conflicting, or incomplete, the model still produces an answer—because generating an answer is its core function. 

That confident output, produced without sufficient grounding, is what we call a hallucination. 

Why Regulated Medical Writing Is Especially Exposed to Hallucinations

Clinical and regulatory documents routinely involve incomplete datasets, evolving safety signals, and nuanced interpretation. These are normal features of regulatory science, but they are challenging for probabilistic models. 

From an AI perspective, uncertainty increases the likelihood of plausible but unsupported text. This is why hallucinations may appear more frequently in regulated content than in general informational writing. 

How Hallucinations Are Commonly Triggered 

Hallucinations are often encouraged unintentionally. Broad prompts, blended objectives, unclear source hierarchies, or requests for “complete” narratives can all push a model to fill gaps that should remain explicit. 

The model isn’t being careless. It’s doing exactly what it was designed to do. 

Why Instructions Alone Don’t Solve the Problem 

Asking AI to “avoid hallucinations” or “only use verified sources” doesn’t change how the model works. What does reduce hallucination risk are structural controls: constrained retrieval, explicit source prioritization, and workflows that clearly separate evidence from interpretation. 

These controls mirror long-established regulatory best practices. AI simply makes their absence more obvious. 

What Hallucinations Reveal About Human Oversight 

Hallucinations highlight where medical writers add irreplaceable value. They expose the moments where judgment, accountability, and interpretation matter most. 

AI can accelerate drafting. It can’t determine acceptable risk. 

That boundary isn’t a flaw—it’s essential. 

Designing AI Systems That Expect Uncertainty 

The most responsible AI systems don’t promise to eliminate hallucinations entirely. Instead, they’re designed to surface uncertainty, preserve traceability, and support human review. 

That’s how regulatory work has always functioned. AI just forces us to be explicit about it. 

Connecting Hallucinations to System Design 

If hallucinations are a symptom of uncertainty, system architecture determines how visible—and manageable—that uncertainty is. Our earlier post, Tokenization: When One Word Becomes Many Problems in AI-Assisted Medical Writing, explains another structural constraint that often contributes to misleading outputs. 

For a broader discussion of hallucinations, you may also want to read Understanding Confabulations in AI: Causes, Prevention, and Detection. 

At Synterex, we design AI-enabled medical writing systems that anticipate uncertainty rather than trying to smooth it away—prioritizing source integrity, human oversight, and regulatory accountability. Learn more at www.synterex.com.