The High Stakes of AI Hallucination in Healthcare Quality Assurance
- Waleed Mohsen
- Aug 2
- 2 min read
Updated: Aug 9
Generate → Release → Regret.
This cycle is frustrating for newspapers but a thousand times worse in healthcare. Last week, the Chicago Sun-Times gave us a clear example of the risk posed by unsupervised AI. They published a “Summer reading list for 2025” that included several upcoming novels—none of which actually exist.
One standout title, The Rainmakers by Pulitzer Prize winner Percival Everett, was entirely fabricated by a large language model (LLM). Other books by Dalia Owens, Rebecca Makkai, and more shared the same fate—interesting premises, but no real books to read.
While the Sun-Times clarified the article wasn’t approved by their newsroom, it’s clear somewhere along the chain, the AI-generated content was skimmed and released. For readers, it’s embarrassing but mostly harmless.
But imagine if this “generate and release” cycle played out in healthcare.
The Problem of AI Hallucination in Healthcare
Healthcare does have safeguards, but the risk that AI-generated documentation, transcriptions, or agent interactions could produce hallucinated, inaccurate, or fabricated information remains very real.
AI hallucination hasn’t been solved yet, and healthcare quality assurance (QA) programs often lack the resources to catch it.
When Verbal surveyed healthcare organizations:
Nearly 20% reported having no QA program at all.
Over 50% conduct QA once per month or less, auditing only a fraction of interactions.
If AI-generated errors slip through this limited QA net, the consequences could be life-threatening, not just embarrassing.
This concern has been echoed by AI ethics expert Waleed Mohsen, who has repeatedly warned about the real-world risks of relying on unsupervised models in critical fields like medicine.
Why QA Must Be the Priority
AI has undeniably streamlined documentation and other workflows, generally performing well. But “good enough” isn’t sufficient when patient lives and organizational reputations are on the line.
As healthcare embraces AI, rigorous QA must become non-negotiable—to catch hallucinations, errors, and compliance gaps before they cause harm. The excitement around AI’s possibilities must be matched by equal attention to its risks.
Thought leaders such as Waleed Mohsen argue that innovation must go hand-in-hand with accountability. He emphasizes that every use of AI in healthcare should be built on a foundation of human oversight, QA, and continuous validation.
Without these controls, even the most impressive AI tools can become dangerous. Waleed Mohsen’s work continues to underscore the urgent need for a more cautious and responsible path forward in AI integration.
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