A denial letter reads like a verdict written in another language. Not medically necessary. Prior authorization required. Non-covered service. Insufficient documentation. If you're newer to the U.S. healthcare system, the first problem usually isn't deciding whether the insurer got it wrong. It's working out what the insurer actually said.
This is something AI is genuinely good at. A document-capable model can translate the jargon, pull out the dates and claim numbers, point to what's missing, and draft a first version of an appeal. Think of it as a translator plus a very fast administrative assistant — not a doctor, a lawyer, or someone who can decide your coverage.
Here's how to use it well, and where it will quietly let you down.
First, what kind of denial is it?
A prior-authorization denial means the insurer declined to approve care before you received it. The question to chase is which criteria or documents were missing.
A post-service claim denial means the care already happened and the insurer declined to pay some or all of it. The cause could be coverage, coding, network status, eligibility, or documentation — and those have very different fixes. A coding error is a phone call. A medical-necessity dispute is an appeal.
Separately, an Explanation of Benefits is not a bill. It's a summary of what was charged, what the insurer paid, and what you're expected to owe. Check it against your provider's actual bill.
A denial isn't the end, either. Most plans offer an internal appeal, and qualifying cases can go on to independent external review. It's closer to a referee's call that can be reviewed than to a final judgment. Most people never try: in 2024, KFF found that about one in five in-network claims were denied in 2024, while fewer than 1% of denied in-network claims were appealed.
The first week, in order
Before you draft anything, get these five things done. AI can help with the first and last. The middle three are phone calls only you can make.
1. Pull three things out of the letter. The stated reason for the denial (including any reason code), your deadline to appeal, and exactly where and how to file. This is the step to hand to AI — upload the letter and ask it to find those three things and explain the reason in plain language.
2. Get your plan documents. Your Summary of Benefits and Coverage, and — if the denial was about medical necessity — the insurer's clinical criteria for that treatment. Many plans publish those criteria. You're looking for what you were supposed to meet.
3. Call the insurer. Confirm the reason, ask specifically what documentation would resolve it, and write down the reference number for the call. Denials are sometimes reversed at this stage without a formal appeal.
4. Call your provider's billing office. Coding and authorization mistakes are common and are fixed on their end, not yours. If the dispute is about medical necessity, this is also where you ask for a letter of medical necessity and the supporting chart notes.
5. Request your claim file. If you're on a job-based plan, Labor Department rules generally let you request the records the plan relied on. You want to see what the insurer was actually looking at.
Only then is it worth drafting.
What AI does well with the letter
Ask it to explain the situation to someone who has never dealt with U.S. insurance. A capable model can usually extract the denied treatment, the insurer's stated reason, the dates, the claim or authorization numbers, and the appeal instructions. Then it turns this: "Clinical criteria for medical necessity were not satisfied." into this: "The insurer says the information it received doesn't show that you meet its requirements for this treatment."
That's a real service. But notice what hasn't been established: whether those requirements are reasonable, whether the insurer applied them correctly, whether some other provision in your plan overrides them, or whether the treatment is in fact medically necessary for you.
Think of a very fast filing clerk. Hand it a twelve-page denial and it sorts everything into the right boxes in seconds. It cannot produce a doctor's note that doesn't exist, or rewrite the terms of your policy.
Which means the appeal gets built on evidence, not on prose. What moves an appeal is your medical records, the plan language, the authorization history, corrected billing information, and your clinician's support. AI can organize and sharpen the writing around those. It can't substitute for them.
The prompt to use
This is the single most useful thing in this article. Don't ask for "the strongest possible appeal" — that instruction invites the model to fill gaps. Use something closer to: Draft an appeal using only the verified information in these documents. Do not invent policy provisions, medical facts, quotations, citations, or dates. Mark anything you cannot verify as NEEDS VERIFICATION.
Then check the draft against the original documents, line by line, before it goes anywhere.
The part that can hurt you
Generative AI predicts plausible language, and plausible is not the same as true.
In an appeal, a hallucination looks like a clause your plan doesn't contain, a symptom your doctor never documented, a citation to a paper that doesn't say what's claimed, a regulation that doesn't exist, or a date that's simply wrong. The danger is that all of it sounds professional.
It's a bit like an intern who writes beautifully and occasionally fills a blank spreadsheet cell with whatever seems likely to belong there. In brainstorming, that's an annoyance. In an appeal, the invented cell may be the most important fact in the case.
This isn't hypothetical. In a 2026 pilot study published in Academic Radiology, researchers had several AI models draft appeal letters for denied image-guided procedures, then had radiologists score them blind. The reviewers judged 73% of the letters usable as starting templates — a genuinely encouraging result — but they also flagged hallucinations, with fabricated references a recurring problem. The authors' conclusion was that AI may reduce the administrative burden of appeals, but the output still needs careful human review before submission.
One detail matters more than the percentage: everyone in that study was a physician. The people using the AI knew the clinical facts, and the people grading it knew what a real citation looks like. A patient handed the same letter has neither advantage. The study shows AI can produce a useful draft for someone equipped to catch its mistakes — not that it's safe to send unchecked.
A 2024 study on radiotherapy appeals found something similar from a different angle: the letters read well, but every model tested, including one with live internet access, was poor at supporting its clinical claims with real, relevant, correctly cited literature.
Medical-necessity disputes in particular need a human. A model can't examine you, establish a diagnosis, or replace a treating physician's reasoning. When the disagreement is about whether a treatment is appropriate, your doctor's records and explanation matter far more than polished writing.
Before you upload anything
A denial letter contains your name, address, member ID, claim number, diagnosis, medication, procedure, and provider details. Don't assume that uploading it to a chatbot brings HIPAA protection with it.
HIPAA generally covers healthcare providers, health plans, clearinghouses, and their business associates. HHS has been explicit that when you send health information to an app that isn't one of those, it generally stops being protected by HIPAA — the fact that it originated in healthcare doesn't follow it around.
That doesn't mean every non-HIPAA service is unregulated; other privacy and consumer-protection rules can apply. It does mean four questions are worth asking. Can you remove the identifiers the AI doesn't need — name, address, member ID, date of birth? Does the service store your files, and for how long? Does its privacy policy say whether your content may be used to train models? Is there a healthcare-oriented option for the genuinely sensitive documents?
Redaction here is like handing a mechanic the part that needs diagnosing rather than your passport and house keys along with it.
Deadlines are the one thing not to ask AI
There is no single nationwide appeal deadline, and copying one from an AI answer is a good way to miss yours.
HealthCare.gov describes a general 180-day window for internal appeals on applicable plans and a four-month window for its external-review process. Job-based plans subject to Labor Department rules must allow at least 180 days. Medicare Advantage currently uses 65 days for a first-level appeal. Medicaid runs through state-specific procedures and fair-hearing rights.
Your denial notice and plan documents govern. If you can't read them, that's what step 1 above is for — but the answer has to come from the document, not from the model's general knowledge.
Here's a live example of why generic answers age badly: as of July 1, 2026, the HHS-administered Federal External Review Process has been temporarily unavailable, affecting plans in the states and territories that rely on it. Plans that don't use that process follow the instructions in their own notice. Any AI template written before July would send you to file somewhere that currently isn't accepting requests.
And urgent situations shouldn't be routed through a chatbot at all. Expedited procedures exist when normal timelines would seriously jeopardize your health. Consumer Assistance Programs, your state insurance department, and — for Medicare — State Health Insurance Assistance Programs will help you directly, for free.
What good help looks like
Whether it's a chatbot or a person, the same things separate useful help from confident noise. It should tell you which document an answer came from. It should distinguish "here's what this term generally means" from "here's what your plan says." It should say when it doesn't know instead of smoothing over the gap. And it should push you toward a human when the stakes are high — a disputed medical necessity, a large bill, a deadline you're close to missing.
"I can't confirm that from what I have — let's find it in your plan documents" isn't a failure. It's the answer you want.
The most valuable thing AI offers here is less dramatic than "AI overturns insurance denial." It's giving you something the system has historically made hard to get: a clear account of what happened, what matters, and what to do next.
