What Happens When AI Gets Your Diagnosis Wrong

You probably use AI for all kinds of everyday things, and if you’re a reasonable person, you’re aware of the fact that your know-it-all bestie can make mistakes, so you take whatever it says with a grain of salt.

But today, AI is used in all sorts of settings/places. And this includes both hospitals and clinics.

But hospitals deal with human lives. What happens if a clinician, a doctor, relies on an AI tool, and the AI ends up making a mistake? What happens when AI endangers human life?

Of course, the AI that’s used in a hospital setting is nothing like your everyday ChatGPT, Claude, Gemini, or any such basic go-to AI tool. But regardless, the ‘A’ in AI stands for artificial. This means that humans created it. And humans trained it. And at the end of the day. It is an automated tool that doesn’t see the world the way humans do. It doesn’t deal with feelings, with intuition, with personal experience. It deals with data. Massive amounts of it.

And if that data gets misinterpreted in any way, a life could be at stake, regardless of how sophisticated the system is. And the most dangerous thing about AI is how convincing it sounds when it messes up.

It’ll back it up with facts and research, and it’ll sound logical. And if you take such a mistake for granted, thinking it’s the real thing. What happens then?

But who cares about that if a person ends up with a heart attack because of a mistake AI made?

Right?

Well, not exactly. And this is what this article will focus on.

Why AI Can Get a Diagnosis Wrong

Why does AI even make mistakes? Let’s look into that first.

If you want the short answer, then you need to know that AI isn’t actually THINKING. All it does is match patterns. Think of it like a super-human librarian who has every book memorized. So all you have to do is ask a question, and it’ll provide your query with an answer based on the query’s intention.

The problem is that all information has blind spots, and if that’s the case, then the librarian had blind spots, too.

And that’s why AI is limited.

AI can learn a great deal from historical medical data, which is extremely useful. However, it also learns any mistakes and biases that were in that data, which means it can’t be 100% accurate.

For example, if AI was trained on data for a condition that’s underdiagnosed for women, then it’ll be less accurate for women. It doesn’t know the gaps even exist, let alone how to solve them.

Plus, if you put garbage in, garbage (usually) comes out.

If you type in ‘chest pain’ instead of the more elaborate ‘sharp, stabbing chest pain that gets worse with breathing’, you’re withholding very important information, and AI loses clues. A doctor can ask a patient to clarify further, but AI can’t, so it takes what you give to it and works with it (very confidently).

AI is in a different dimension when it comes to recognizing patterns. From a human perspective, it’s beyond savant-level. With that being said, you can’t really treat people without clinical reasoning. And AI cannot do that.

Sure, AI can spot a microscopic fracture on an X-ray that even the best radiologists might’ve missed. But as soon as you challenge the AI by giving it something unusual, or something complex, the results turn into a (potential) disaster.

AI has no imagination. It has no creativity. It isn’t curious. It cannot think outside of the box like humans can.

You can make the AI be human-like. But it will never be human. Perhaps, one day we might bring the simulation as close to a human consciousness as we can. But even then, it’ll have limits.

The only thing it can really do is recognize what it has seen before.

So, what do you do when you make a mistake?

At that point, the documentation is everything.

Of course, the doctor will order some extra tests and look over your diagnosis once more in order to help you get better. But what’s also going to happen is that they’ll also review every single piece of documentation to find exactly where the mistake originated.

If you’re wondering what you should do when this happens, there are a few things you need to know.

AI is used for diagnostics all over the U.S., and the legal processes for medical errors vary depending on the state. What this also means is that the written standard for proving negligence, as well as the deadlines for filing such a claim, are different based on jurisdiction.

Nashville injury lawyers can only help you if you’re in Tennessee; that same Nashville lawyer wouldn’t be able to do anything in Kentucky or Alabama because the laws are different there (unless they’re licensed to practice law in a different state).

And this is very important to know for both patients and hospital staff, because if AI messes up, it’s the lawyers that will handle the aftermath of what has happened.

What the Healthcare System Is Doing to Reduce AI Diagnostic Errors

Everyone’s aware of these mistakes. They exist. This is the first step.

With that in mind, what we can focus on is how to prevent them, or at least, how to bring them to an absolute minimum.

This is where human intervention comes into play. Let the AI do the time-consuming heavy lifting (e.g., the investigation, the analysis, etc.). But there needs to be a human filter that makes sure the output is considered ‘safe’.

If it isn’t, it’s mapped and sent back to the AI. If it is, only then does it go forward.

The AI tool needs to be tracked and monitored at all times. Nothing should be taken for granted. The more complicated the case is, the more caution is required. Besides, all tools are tested independently before they even reach a clinic.

Transparency is getting better, as well, so even when the tool does make a mistake, it’s easier to see why it happened.

The good news is that AI keeps getting smarter, but as of right now, it’s still being used only as a supportive tool.

Conclusion

Have no fear, doctors aren’t getting replaced anytime soon. As useful as AI is, it has no idea when it’s wrong and when it needs to ask for clarification.

AI is only a tool; it’s not a colleague, and that’s exactly how it should be (for now, at least).

The healthcare system isn’t planning on choosing between a person and a machine because everyone knows that they work best together.