What’s the difference?
- Hallucination: A hallucination occurs when an AI system generates information that is entirely fabricated—something that doesn’t exist in its training data or in reality. Example: An AI tool invents a medical condition or cites a study that was never published.
- Confabulation: A confabulation happens when the AI misrepresents or distorts real information. It may cite a legitimate source but misinterpret its findings or apply them incorrectly. Example: An AI system attributes a symptom to the wrong condition or misquotes a clinical guideline.
Why it matters in healthcare
In clinical settings, the consequences of misinformation can be serious. Whether fabricated or distorted, inaccurate AI outputs can lead to misdiagnoses, inappropriate treatments, and a loss of trust in digital tools. That’s why distinguishing between hallucinations and confabulations is more than academic—it’s essential for patient safety.
Five ways to prevent AI hallucinations and confabulations
- Use high-quality, domain-specific training data: Train AI on peer-reviewed studies, clinical guidelines, and verified medical records—not general internet content.
- Implement robust validation and testing: Regularly test AI systems against real-world scenarios and edge cases to catch inaccuracies early.
- Incorporate human oversight: AI should support—not replace—clinical judgment. Human review is critical.
- Use confidence scoring and explainability: Let users see how confident the AI is in its answers and understand how it reached its conclusions.
- Restrict outputs to verified knowledge sources: Limit AI responses to trusted databases like UpToDate® to reduce the risk of misinformation.
How we help
At Wolters Kluwer, our clinical decision support solutions are built on evidence-based content, helping to make sure that care decisions are grounded in trusted knowledge.
