What are hallucinations in AI?

In simple terms, an AI hallucination occurs when an artificial intelligence model produces false, fabricated, or misleading information that appears correct. The model isn’t being deceptive but rather it is doing what it was trained to do: predict what comes next based on patterns in data and to be helpful. When the data or prompt lacks grounding, the model may fill in the blanks with confident but incorrect information.

Why AI hallucinations happen

Large language models are trained on massive amounts of text data from across the internet. These models learn to generate language that sounds reasonable but the models can’t have an inherent understanding of truth or accuracy. As these models are trained to be helpful to the user during the RLHF steps, without built-in mechanisms for fact-checking or validation, they can easily produce outputs that look authoritative but aren’t.

In consumer applications, this might lead to an AI chatbot providing a wrong trivia answer or inventing a fake citation. In a business context, it might produce a report that looks data-driven but includes incorrect assumptions.

In healthcare, however, the stakes are far higher. A hallucination in a clinical AI system could mean:

Referencing the wrong medical guideline
Suggesting a treatment outside of standard of care
Misinterpreting a utilization management policy
Generating incomplete or non-compliant rationale for prior authorization decisions

These types of errors in the healthcare industry have a large and dangerous impact as they represent real risks to patient safety, provider trust, and payer compliance.

Why hallucinations matter in healthcare AI

Healthcare operates on the highest standards. Every clinical or administrative decision must be evidence-based, traceable, and compliant with policy. When AI enters that environment, accuracy and explainability become non-negotiable.

That’s why healthcare-native AI platforms are designed differently from general-purpose tools. They are built to reason within a structured, auditable framework that prioritizes clinical accuracy, not just language fluency.

Anterior’s platform not only documents outcomes from AI reasoning, but also the AI reasoning itself. Each decision can be traced, cited, and reviewed by a clinician. This creates an environment where AI doesn’t just make decisions in a black box but in fact supports actual clinical reasoning and accountability.

How healthcare AI can reduce hallucinations

The best approach to managing AI hallucinations in healthcare combines technical design and clinical governance:

Explainable AI: Every output includes a clear rationale and references to source material.
Human-in-the-loop oversight: Clinicians validate, audit, and refine model outputs to maintain accuracy.
Domain-specific training: The model should be trained on medical policy, evidence-based guidelines, and payer workflows—not just general internet text.
Continuous feedback loops: The system learns from real-world usage to improve helpfulness without sacrificing safety.
Data governance and compliance: All data handling meets HITRUST and HIPAA standards, ensuring security and traceability.

This framework makes AI safer, more reliable, and more compliant with the complex requirements of payers and providers.

The bottom line

AI hallucinations are not just a technical curiosity — they are a core challenge in deploying AI responsibly, especially in healthcare. General-purpose AI can be creative and conversational, but healthcare AI must be grounded, auditable, and clinically accurate.

Hallucinations might not be eliminated entirely, but they can be minimized, managed, and fixed through strong design, continuous oversight, and healthcare-native engineering.

Key takeaways