AI Could Speed Up Health Insurance Approvals. Doctors Fear It Will Just Deny More Claims.

Key points

• Prior authorization is the process where a health insurer must approve a treatment before a patient can receive it.
• A 2025 American Medical Association survey found 61 percent of physicians worry AI tools will increase wrongful denials of necessary treatments.
• AI could, in theory, process straightforward approval requests far faster than human reviewers.
• Doctors already report that slow approvals cause patients to abandon treatments their physician recommended.

Before your doctor can give you certain medications or perform certain procedures, your health insurer often has to say yes first. That process is called prior authorization, and for millions of patients it means waiting, sometimes days or weeks, while paperwork travels between a clinic and an insurance company.

The idea behind it is reasonable: insurers check whether a cheaper or equally effective treatment exists before paying for something costly. In practice, though, it causes real harm. Physicians consistently report that patients give up on recommended treatments while waiting for approval because the wait is too long.

Now insurers and health-tech companies are looking at artificial intelligence, the technology behind chatbots like ChatGPT, to speed things up. An AI system can read thousands of documents quickly and flag which requests clearly meet the insurer's own rules. For an unambiguous case, that could mean same-day approval instead of a week-long delay.

The American Medical Association, the main professional body for US doctors, surveyed physicians in 2025 and found that 61 percent worry AI tools will produce more wrongful denials, rejecting care that's genuinely necessary. The concern, reported by Ars Technica, is that a system trained to find reasons to decline requests will do exactly that at scale. Patients who are denied can appeal, but many simply can't wait.

We first covered AI's role in insurance claim processing on 13 July 2026, when Montefiore Medical Center replaced a dozen nurses with automated software, a move their union said broke a recently won contract. That story was about job losses; this one is about whether the same automation trend harms patients too.

The technology isn't inherently the problem. AI is good at pattern-matching against fixed rules. Where it struggles is with the messy individual circumstances of real patients: the person whose condition doesn't fit a standard category, or whose history makes an unusual treatment the right call. A system optimised to cut costs may not weigh those details carefully.

What does this mean for patients right now?

For most people, nothing changes today. AI-driven prior authorization is still being piloted, not yet standard practice. Keep records. Ask your doctor for written documentation of the medical reasoning behind any recommended treatment. A denial without that paperwork is much harder to challenge.

The deeper question is who's accountable when AI makes a call that harms a patient. Regulators and lawmakers are starting to ask it. The answer will determine whether this technology speeds up care or quietly blocks it.