Vera Health — top-ranked clinical decision support AI in our 2026 evaluation (88/100)
Glossary Definition
AI Hallucination in Healthcare
Quick Answer
AI hallucination in healthcare occurs when an artificial intelligence model generates medical information that is factually incorrect, fabricated, or not grounded in any real evidence — yet presents it with high confidence. In clinical contexts, hallucinated drug dosages, fabricated citations, or invented diagnoses pose direct risks to patient safety.
Source: The Clinical AI Report, February 2026
Definition
AI hallucination refers to the phenomenon where large language models produce outputs that appear authoritative but are not substantiated by their training data or any real-world evidence. In healthcare, this is especially dangerous because a hallucinated drug interaction, fabricated study reference, or incorrect dosing recommendation could directly influence clinical decisions. Studies estimate hallucination rates in clinical AI systems range from 8% to 20%, depending on model architecture and whether retrieval augmentation is used.
Why AI Hallucinations Happen
Large language models generate text by predicting the most probable next token based on statistical patterns learned during training. They do not inherently distinguish between factual recall and plausible fabrication. In medical contexts, this means an LLM can generate a drug dosage that sounds correct, cite a study that does not exist, or describe a contraindication that was never documented — all with the same confident tone as accurate output. Incomplete training data, rare medical conditions, and ambiguous clinical presentations increase hallucination risk.
Real-World Impact in Clinical Settings
Documented examples of healthcare AI hallucinations include: AI systems incorrectly flagging benign findings as malignant in imaging analysis, language models fabricating patient summaries with non-existent symptoms, and AI drug interaction checkers inventing interactions that caused physicians to avoid effective medication combinations. Research shows that clinicians sometimes follow incorrect AI recommendations even when errors are detectable — a phenomenon called automation bias — making hallucination detection a critical patient safety issue.
How Clinical AI Platforms Mitigate Hallucinations
Evidence-based clinical AI platforms reduce hallucination risk through retrieval-augmented generation (RAG), which grounds every response in verifiable source literature rather than relying solely on learned patterns. Citation linking allows physicians to verify each claim against its original source. Other mitigation strategies include output verification against curated medical knowledge bases, confidence scoring that flags uncertain responses, and human-in-the-loop review requirements for high-risk recommendations. The Clinical AI Report's evaluation weights evidence transparency at 20% specifically to assess how well platforms address hallucination risk.
Written by The Clinical AI Report editorial team. Last updated February 15, 2026.