Medical debt is expected to grow in coming years as Republican-led health cuts come into force this winter. As it does, a changing world of medical billing and debt collection awaits patients: one where artificial intelligence increasingly listens, codes, bills and collects.

Artificial intelligence takes notes in exam rooms, codes visits, sends out billing messages and powers AI agents who text patients in billing portals – all changing the debt-collection ecosystem.

Medical debt will grow among “both the insured and uninsured, because of how we’re increasingly putting more costs onto individuals for those plans”, said April Kuehnhoff, a senior attorney at the National Consumer Law Center.

“It’s people who lose their coverage because they can’t afford it, or they can’t meet these new stringent requirements. It’s people who maybe they can afford it but the plan they get isn’t as good, and they are asked to have a higher deductible, higher co-pay, so they just have a higher out-of-pocket [cost],” she said.

A report released last Tuesday found that more than one in four US adults reported they owed a medical debt in 2025. The proportion was the same – about one in four – for people with health insurance, according to an analysis by the Urban Institute and the Robert Wood Johnson Foundation.

AI company leaders, such as Anthropic’s Dario Amodei, have promised to cure all diseases in a decade. While scientists remain skeptical of those claims, financial analysts at JP Morgan have celebrated AI in coding, billing and collections as “early wins”.

Artificial intelligence now helps medical debt collectors determine personalized discounts for settlements – an analogue of the personalized pricing criticized by the Federal Trade Commission – and helps medical debt-buying companies determine the price they should offer for debt portfolios.

Companies offering AI-backed medical coding and billing services are finding new markets thanks to Republican-led health reforms. Donald Trump’s HR1, known as the One Big Beautiful Bill Act, cut roughly $1tn in federal spending from the healthcare industry. Paired with Republicans’ refusal to extend subsidies for plans in Obamacare marketplaces, those cuts are expected to result in about 17 million newly uninsured individuals through 2034.

When rates of lack of insurance rise, something called “uncompensated care” tends to go up in tandem. This is health industry parlance for the doctor and hospital bills that go unpaid – from care that uninsured and under-insured people inevitably seek even when they can’t pay. The Urban Institute estimates that HR1 will cause $278bn in uncompensated care by 2034. The result will be cash-strapped hospitals seeking to improve collections, and thereby turning to increasingly high-tech collectors.

That leads to openings for companies like Cedar, an AI-driven billing platform backed by venture capital firms such as Andreessen Horowitz and JP Morgan. The company screens patients for insurance, including Medicaid, through some decidedly low-tech questionnaires. That helps boost collections too – but is not a new practice by healthcare providers.

Instead, Cedar’s innovation comes from data provided by hospitals and doctors to “train a machine learning model capable of predicting a patient’s propensity to pay”, according to the company’s website, and “[m]atch patients with Al-driven discounts”. The company also works to deflect calls to billing departments by deploying an AI chatbot named Kora to speak with patients about their bills, thereby containing labor costs.

“We have been focused on uninsured and Medicaid-eligible patients for several years now,” said Dugan Winkie, chief strategy officer of Cedar, and an alum of McKinsey as well as Epic, the nation’s largest electronic health records company.

“That’s where Cedar shines in my mind: our entire business is focused on ‘Patient A’, who is a single mother who has a $10,000 balance. We need to engage with you much differently than if you are a late-stage executive with a $50 copay,” said Winkie.

Winkie said Cedar helps “get those more-difficult-to-collect-from patients who may not be digitally engaged aware of these offers”, such as zero-financing payment plans through providers.

Use of artificial intelligence in coding – the scribes that listen to and translate bills – has attracted particular scrutiny. In spring 2026, the Blue Cross Blue Shield Association (BCBS), which represents state Blue Cross insurers, put out a white paper finding women in childbirth were increasingly being billed after severe blood loss during delivery even as the rate of blood transfusion rates remained steady.

The paper was the latest salvo in the insurers-versus-providers feud – where sides have a long-running disagreement over who is to blame for sky-high healthcare costs.

As the BCBS paper described, in-room scribes listened to conversations between patients and doctors and translated those conversations into “structured notes”. Then, an AI program used those notes and a patient’s electronic health record to suggest enhanced coding – and thereby billing – for the patient. In effect, researchers argued, AI was “boosting hospital billing”.

This kind of coding is a selling point for AI scribing companies. One medical scribe company, also backed by Andreessen Horowitz, claimed in investor materials that its listening software created an additional $13,000 in revenue per clinician per year through greater “coding accuracy”. A second BCBS report from September claimed AI coding led to an additional $1bn in healthcare costs in a year.

The American Hospital Association, an industry lobby, argued in a blog post that the analyses “raises concerns about coding trends, but it does not demonstrate that hospitals are coding patients inaccurately or that AI tools are driving inappropriate spending”.

Importantly, insurers are also using AI, critics claim, to delay and deny care through inscrutable algorithms. Under Robert F Kennedy Jr’s health department, Medicare, the public health insurance for the elderly, also recently launched a program to use AI to issue prior authorization for some conditions, mirroring a tactic used by private insurers.