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When AI Gets It Wrong: The Real-World Impact of AI Hallucinations in Medicine

We’ve all seen the explosion of AI tools offering quick answers and insights—and in many ways, they’ve been a game changer for research and innovation. But when it comes to complex topics like medical research, aesthetics, and plastic surgery, accuracy is everything. While AI can be incredibly helpful, there have been several cases where it has sourced incorrect or misleading information—something we can’t afford in this field.

If you’ve ever asked an AI tool about a procedure or provider and received information that sounded convincing but turned out to be totally false, you’ve seen firsthand what researchers and medical professionals call an AI hallucination. In this blog, we’re digging into what these hallucinations are, why they happen, and how they can affect both patients and doctors.

What Are AI Hallucinations?

In plain terms, AI hallucinations happen when a model like ChatGPT or Google Gemini makes something up and presents it as fact. This can include fabricated statistics, fake journal citations, or invented doctor reviews. These tools are trained to sound confident, even when they’re flat-out wrong.

This happens because AI models are trained on vast amounts of text from the internet, which includes both accurate and inaccurate information. They don’t actually "know" facts—they generate responses based on patterns in language. So if something sounds like it fits, the AI may present it as true, even if it’s entirely made up. The goal of these models is to be coherent and human-like, not necessarily to be correct, which is why hallucinations can occur—especially in technical or specialized fields like medicine. For example, when researching plastic surgeons, an AI might mistakenly attribute a negative review or a malpractice case to the wrong doctor, damaging a reputation based on entirely false information.

A 2023 paper in Nature defines hallucinations as “outputs that are nonsensical or unfaithful to the provided input,” and they’re especially dangerous in medicine, where trust and accuracy are everything. A Stanford University study found that even advanced AI models produced hallucinated citations in over 40% of medical prompts.

How This Affects Patients

Let’s say a potential patient searches, “Best plastic surgeon in Birmingham” or “What’s the safest breast implant technique?” They plug it into an AI tool—and get results that:

  • Name the wrong provider for a particular procedure
  • Attribute false credentials or reviews
  • Recommend outdated or even unsafe treatments

Patients may take those answers at face value. After all, it sounds official. But, misinformation can shape expectations, cause confusion during consultations, or steer people toward unverified providers.

Even worse? These hallucinations often get reinforced by clickbait blogs, aggregator sites, and social media influencers who prioritize speed over accuracy.

How It Can Hurt Doctors and Practices

If you’re a provider, AI hallucinations can damage your reputation without you even knowing it. Many AI-generated summaries reference procedures you don’t offer, misquote your credentials or lift outdated bios from third-party sites. We've seen AI models invent entire surgical techniques and attribute them to the wrong surgeons.

But patients are impacted too. Imagine someone choosing a surgeon based on inaccurate claims—thinking a certain technique is offered, or that a provider has experience they don’t actually have. Or worse, mistaking one surgeon’s review for another. Now, you’re not just correcting misinformation—you’re rebuilding trust and re-educating someone who came in with false expectations.

Why You Should Use Trusted Sources

This isn’t about rejecting AI altogether. It’s about knowing where to go when accuracy matters. When researching any plastic surgery procedure or provider, here’s what you can trust:

  • Board Certification – Look up your surgeon on The American Board of Plastic Surgery.
  • Official Websites – Rely on practice-owned websites with verified bios, real before-and-after photos, and direct contact info.
  • Google Reviews and Maps – Check what real patients are saying (and notice how often they mention specific outcomes).
  • Google Business Profile – It’s updated by the provider and reflects current services and hours.
  • Published Articles – For data, go straight to published research, not AI summaries.

If you're unsure, call the practice directly. The front desk or patient coordinator will give you better information than a bot ever could.

So Why Does AI Do This?

AI models are trained on massive datasets scraped from the internet, but they don’t understand truth. They understand patterns. When asked a question, they try to produce something that sounds right—even if they have to invent it.

In fact, the more specific your question, the more likely an AI is to fill in the blanks with something completely made up. When a user asks, “Who’s the top plastic surgeon who uses the endoscopic method?” the AI may combine unrelated facts, guess names, and fabricate entire accolades.

As one Harvard review noted: "These systems are optimized to be plausible, not accurate."

Final Thoughts: Use AI Carefully, Not Blindly

We get it—AI is fast, convenient, and full of potential. It can be a helpful starting point for learning and exploring new ideas. But when it comes to important decisions about your health, your body, or your practice, accuracy matters. Whether you’re a patient researching treatment options or a provider safeguarding your reputation, think of AI as a powerful assistant—not the final authority. Feel free to use it, but always verify with trusted sources, and when in doubt? Turn to a qualified human expert.

Want real answers from real experts? Contact Core Plastic Surgery directly to speak with a trusted team led by board-certified plastic surgeon Dr. Grady Core. No hallucinations—just facts, experience, and results you can trust.

References:

  • Ji, Z., Lee, N., Frieske, R. et al. Survey of Hallucination in Natural Language Generation. ACM Comput. Surv. 2023.
  • Shen, Y., Heacock, L., Elias, J. et al. ChatGPT and Other Large Language Models Are Double-Edged Swords. Nature Medicine. 2023.
  • Gupta, R., Katarya, R. Understanding AI Hallucination and Mitigation Strategies. IEEE Access, 2023.
  • Stanford Center for AI in Medicine & Imaging. 2023 Report on LLMs in Clinical Practice.
  • Harvard Kennedy School Misinformation Review, 2023.

We are happy to answer any questions you may have and get you on your way to beautiful, natural-looking results. Contact us.

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