Is AI sending too many people to the ER? Docs debate the new wave of algorithm-driven anxiety

A patient gets a cat bite, asks an AI chatbot what to do, and is told to seek emergency or urgent care. Another notices left-sided chest discomfort, consults AI, and quickly spirals toward panic after being warned about potentially serious causes. 

Neither scenario would have been unusual in the pre-chatbot era—patients have long turned to Google and WebMD before calling a doctor—but clinicians in a recent Reddit discussion in r/medicine say something may be changing: AI is increasingly acting as the first voice in the triage chain.

For doctors, the question is what happens when a tool designed to avoid missing worst-case scenarios gives advice without the clinical context or accountability that normally comes with triage.

Patients aren’t supposed to know what’s an emergency

Not everyone in the discussion saw AI-directed visits as inherently problematic. One commenter, emergency physician @Commander_Corndog, argued that patients should not be expected to independently determine whether their symptoms represent an emergency in the first place. 

“I don’t mind patients coming in because AI told them to. I hate all things AI and I wish we didn't now have a society that is replacing critical thinking with it, but even before AI it was never the laymen’s job to determine what is or isn’t an emergency. That’s our job, which one way or the other requires them to see one of us if they have an actual concern,” they wrote. 

The bigger frustration, according to several physicians, comes after the evaluation. Doctors described patients who arrived with AI-generated differential diagnoses and then pushed back when the clinician’s assessment didn't match what the chatbot had suggested. 

That dynamic can turn AI from a prompt to seek care into something closer to an invisible second opinion—one that has not examined the patient, reviewed the full chart, or taken responsibility for the downstream consequences of its recommendations.

The anxiety problem may be especially tricky

Chest symptoms are a particularly obvious example of the challenge. Left-sided chest discomfort can be associated with serious pathology, but it can also occur with panic, musculoskeletal pain, gastrointestinal symptoms, and countless other conditions. 

A cautious AI system may have good reason to advise medical evaluation when it lacks the ability to examine the patient or reliably assess risk factors. But that same caution can create a feedback loop.

Who pays when AI gets triage wrong?

One EMT in the Reddit discussion raised a broader systems-level concern: over-triage doesn’t happen in a vacuum.

There’s also the financial question. A patient might have been able to see a PCP or urgent care clinician for a fraction of the cost but instead receives a substantial emergency department bill after following chatbot advice.

AI companies, the EMT argued, do not directly bear the financial or clinical consequences when their recommendations send someone to the wrong level of care—or, conversely, when dangerous reassurance keeps someone home.

“If these companies want to make money by selling these decision-making engines, they should also be prepared to pay up when those decisions cause harm. They get all of the reward and none of the risk, and they do it at scale. Patients can’t be expected to distinguish emergent vs urgent vs PCP, but a product operating at scale, particularly one with shareholders who profit from it, should be expected to … and if it can’t, it should exit the playing field,’' they wrote.

Is this really a new phenomenon—or just WebMD with better branding?

Another commenter offered a useful reality check: “Anytime people get a little bit of information, without the total context they can use it incorrectly. I think the AI patients of today are probably the same WebMD pts of 20 years ago,” wrote @16semesters, a nurse practitioner. 

Patients have always arrived armed with partial information. Search engines, symptom checkers, medication interaction tools, and online forums all made medical information more accessible while also making it easier to misunderstand. 

Even a small amount of accurate information can lead to the wrong conclusion when the user doesn’t know how to weigh it against everything else.

What doctors may need to prepare for

The practical takeaway may not be to dismiss every patient who says AI told them to come in. In some cases, the AI will have done exactly what clinicians hope a cautious triage system would do: Encourage someone with a potentially serious problem to seek care.

The challenge for clinicians may be learning how to practice medicine alongside a new participant in the exam room—one that can suggest anything, explain everything, and bear none of the responsibility for being wrong.