I just celebrated a big milestone I hope you never reach: I hit my health insurance plan’s $10,150 out-of-pocket maximum less than five months into 2026, thanks mostly to two major eye surgeries. That means no more co-pays or coinsurance for authorized in-network care this year, as long as I keep paying my monthly premiums.

But earlier this year, as I accumulated what seemed like an eternal fountain of medical expenses, I couldn’t help but wonder whether I was paying bills that contained errors. As a certified financial planner and a longtime personal finance writer and editor, I’m familiar with how many medical bills contain mistakes that make them more expensive. 

Occasionally, medical bills contain obvious errors, such as a charge for a treatment you explicitly declined. Otherwise, though, these mistakes are often difficult for a typical patient to spot. Finding billing errors can require clinical knowledge, along with an understanding of medical coding, revenue cycle management and the opaque American health insurance system. 

You may also have to sift through huge volumes of information. For example, I discovered that I’d had 87 insurance claims during the first four-and-a-half months of 2026 and that the contract I’d signed during open enrollment was 149 pages long.

I had no desire to get an education on medical coding or meditate over the meaning of 149 pages’ worth of insurance jargon, but I thought perhaps generative artificial intelligence would be up for the task. After all, AI excels at taking in complex information and finding irregularities in huge volumes of data.

Turns out, though, using AI to find errors buried in my stacks of medical bills wasn’t as easy as I’d hoped. Here’s how I did it — and what I learned.

How I used AI to search for medical bill errors

I expected to find a plethora of AI tools to help patients identify billing errors. Wrong.

Most AI tools aimed at improving billing accuracy are designed for providers, not patients, for obvious reasons.

The few patient-facing tools that exist often target a fairly narrow segment of billing issues. For example, Counterforce Health uses AI to analyze medical bills and records to help patients understand why their insurance claims were denied and to draft an appeal. But few AI resources for patients exist that offer a general audit of your medical bills.

So I settled on using generative AI — specifically, my $20 monthly ChatGPT Plus subscription, which had already been hugely helpful to me in crafting scripts to use with my insurer when they attempted to deny care.

My step-by-step process:

1. Narrowed my focus to claims where I’d spent at least $150 to simplify the review.
2. Retrieved my 146-page insurance contract and explanations of benefits, or EOBs, from my insurer’s website.
3. Requested itemized medical bills from my providers, which are essential for identifying costs and inaccuracies.
4. Compiled 14 itemized bills and EOBs, along with a spreadsheet summarizing all 87 of my claims.
5. Redacted all personal information — such as my name, date of birth, address and insurance ID number — from the documents before uploading them to the AI.

Then I used the following ChatGPT prompt:

Act as a medical billing expert and auditor with deep knowledge of the US healthcare system, medical billing codes, surgical billing practices and outpatient billing practices. I will provide my insurance contract, an itemized bill and an explanation of benefits. Look for incorrect charges, unusually expensive or questionable charges, mathematical errors, charges that appear inconsistent with my insurance contract and other potential inaccuracies.

Did ChatGPT find medical billing errors?

Before I’d even uploaded my itemized bills to ChatGPT, I could see an obvious flaw: How was AI supposed to know whether the bill accurately reflected the care I received?

For example, the first two itemized bills from the surgical center included 31 to 60 minutes of operating room time. But I hadn’t brought a stopwatch into surgery with me. 

Maybe ChatGPT would have flagged it if I’d been billed for several hours of surgical time for a procedure that usually takes a few minutes. But how would ChatGPT know if, say, I was only in the OR for 28 minutes? Or whether the 200 or so preop eyedrops I’d received were accurately reflected in the itemized surgical bill?

Instead, ChatGPT kept focusing on things such as the fact that the amount my insurer paid looked ridiculously low compared to what the surgeon, anesthesiologist and facility actually billed. Fair enough, but that’s more an indictment of the opaqueness of the American healthcare system than a sign of a billing error.

AI told me to look into the only claim marked “denied” on the spreadsheet. But the reason for the denial was that my surgeon had voluntarily withdrawn and resubmitted it before my insurer had even processed it. A few pharmacy claims had been reversed, but those also had an easy explanation: The pharmacy had automatically processed a couple of refills I hadn’t needed.

I quickly lost hope that AI would help me find potential billing errors that I hadn’t already identified. So I started asking it point-blank questions about specific claims.

There was one potential error I had already spotted: For one procedure, I had been charged both a $100 specialist co-pay and a $150 co-pay for a physician-administered drug, or $250 total. I talked with a customer service rep online who said I should only have been billed for one. So, I uploaded my live-chat conversation with the rep, asking:

This conversation with an insurance representative says that I will only owe a $100 retina specialist co-pay or a physician-administered drug co-pay of no more than $150 for anti-VEGF injections, but I was charged $250 for the visit and injection. Is this an error?

ChatGPT quickly dashed my hopes on that front, directing me to the section of my 149-page insurance contract stating that I was responsible for both co-pays. The insurance rep had clearly been wrong.

OK, but why had I paid $11,512 in co-pays and co-insurance when my maximum patient responsibility was $10,150?

ChatGPT kept insisting that I’d only paid $10,150. Then it hit me: ChatGPT showed that I’d only paid $10,150 because that was my patient responsibility, according to my EOBs.

Three weeks later, I’d had the exact same surgery on my right eye. Since I’d hit my deductible, I’d had to pay a lesser amount: $1,552, which I assumed represented 50% co-insurance. But my EOB listed my patient responsibility at $999.

Again, I asked ChatGPT about the discrepancy. This time, it pointed out something that seems obvious in retrospect.

The $1,552 I’d paid upfront was the amount I was actually responsible for after the first surgery. Since I was having the same surgery on the other eye, the facility had estimated the amount I’d owe based on the first surgery, without accounting for how my patient responsibility would change after I hit my deductible.

So ChatGPT confirmed that I’d overpaid by $1,512 for that second eye surgery, and it helped me understand why. But it didn’t actually find the $1,512 overpayment on its own. I found that by keeping careful records of every medical expense I incurred.

Just supplying ChatGPT with all the information it needed to confirm the error took a huge amount of work. In that respect, it seems as if using ChatGPT to comb through medical bills is a bit like using tax filing software: It’s only as accurate as the data you supply, and gathering all that takes a ton of work.

It’s possible that my itemized medical bills did contain additional errors. If they did, that’s a matter for my providers and my insurer to fight about. As long as I don’t have to pay more than my $10,150 out-of-pocket maximum — and I have no doubt that the amount I’m responsible for as a patient reached that amount — I honestly don’t care if they have to fight between themselves; that’s not my problem.

As of this writing, I’m still waiting for my $1,512 refund.

How to do this yourself

If you want to use AI to help you audit your own medical bills, keep these prerequisites in mind:

• Request itemized bills: You’re entitled to a breakdown of every cost incurred during a procedure. Contact your provider to request this, as it’s essential for identifying specific billing inaccuracies.
• Redact sensitive data: Before uploading any documents to an AI tool, remove all personal information, such as your name, date of birth, address and insurance ID number.
• Maintain a personal spreadsheet: AI is only as accurate as the data you provide. Keep a detailed log of every medical claim, the amount billed, the amount your insurer paid and your actual out-of-pocket payments. This manual tracking is crucial for spotting discrepancies between what you were charged and what you were actually responsible for.