Shawn knight

Posted on Mar 24 • Originally published at Medium on Mar 23

2025 ChatGPT Case Study: Why AI Hallucinates in Medicine (And What It Will Take to Fix It)

#education #learning #chat #ai

Artificial intelligence is changing everything — from business to education to creativity.

But when it comes to medicine , hallucinations aren’t just embarrassing.

They’re dangerous.

And the biggest shock?

AI isn’t hallucinating because there’s not enough data.

It’s hallucinating because of how the system was trained — and how institutions are failing to evolve.

AI Doesn’t Understand Truth

LLMs (large language models) like ChatGPT aren’t built to “know” medicine. They’re built to predict language.

That means they don’t know what’s true. They only know what’s probable.

Give it a thousand research papers, it will average the tone, synthesize the structure, and say what sounds right.

But medicine doesn’t need “sounding right.”

It needs exact.

It needs grounded.

It needs context-aware diagnosis with real consequences.

The Data Isn’t the Problem. The Access Is.

There’s plenty of medical data in the world:

Research journals
Clinical trial reports
Medical textbooks
Physician notes
Hospital records

But most of it is:

Behind paywalls
Messy or unstructured
Contradictory
Legally protected

So GPT gets stuck pulling from summaries, abstracts, and forum posts — not deep, validated, clinical pipelines.

It’s like trying to become a doctor by reading Reddit threads and article headlines.

Medicine Speaks in Probabilities, Not Absolutes

One of the trickiest challenges? Medical language is full of:

“Might be related to…”
“Potential indicator of…”
“Requires further study…”

That nuance is critical for humans. But for LLMs? It creates a fog of ambiguity.

Which is exactly how hallucinations happen:

The model blends three uncertain statements into one fake but confident answer.

Institutions Don’t Want to Open the Vault

Fixing hallucinations would require:

Clear, structured, vetted medical logic chains
Shared definitions across conditions and treatments
Open access to verified outcomes

But here’s the truth:

Pharma companies want to protect intellectual property.
Journals make money from paywalls.
Hospitals and EHR providers sell access to data.

They don’t want to open-source clarity. Because that would mean giving up control.

What Needs to Happen Next

If we really want AI to be safe in healthcare, here’s what must be built:

✅ A Medical Grounding Layer: A logic-driven interface that prevents LLMs from generating medical content unless it can verify across trusted nodes.

✅ Open-Sourced Medical Frameworks: Public, community-backed, constantly updated knowledge structures (even if actual patient data remains protected).

✅ Incentivized Truth Structures: Mechanisms where accuracy is rewarded — not just fluency.

✅ Systems Designed for Execution, Not Just Theory: This isn’t about publishing another paper. It’s about deploying safeguards in real-time.

AI isn’t hallucinating because it’s broken. It’s hallucinating because it was trained to mimic humans without the clarity of consequence.

And until institutions prioritize access, grounding, and transparency , hallucinations will continue.

The solution isn’t more data. The solution is better infrastructure.

The builders who understand this? They’ll lead the next era of AI-powered healthcare.

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