Can AI Help You Appeal Your Health Insurance Denial?

Receiving a health insurance denial can feel demoralizing – especially when the treatment you need is urgent, expensive, or even potentially life-changing. In most cases, you, as the insured person, have done everything right by paying your health insurance premiums and adhering to the company’s rules. Yet here you are, facing a denial right when you need your insurance company to do the right thing. 

The appeals process can be confusing, time-sensitive, and full of technical requirements that can overwhelm even the most organized person. In short, you may feel like you are navigating a complex maze with no map to guide you. The paradox of artificial intelligence (AI) is that it can be the cause of the denial and can help you appeal the denial by analyzing your denial letter and helping you draft a strong, evidence-based appeal.  

In a system where the smallest mistake can lead to adverse consequences, AI can help you respond faster and more effectively to an insurance treatment denial. While AI cannot replace experienced legal guidance, it can give you the information, structure, and strategy you need during this difficult time. If you are facing a health insurance denial, you need a solid legal advocate who is not afraid to step up and fight for you. That advocate is attorney Scott Glovsky and the Law Offices of Scott Glovsky.

What Should You Know About AI and Health Insurance Claim Denials?

Harvard University published an article entitled “AI is Making Medical Decisions — But for Whom?” in May 2025. The subtitle of the article is telling: “Doctors warn that without an ethical framework, patients could be left behind.” The article details an experiment that included 1,000 simulated patient cases. A Harvard professor and editor-in-chief of the New England Journal of Medicine selected 200 pairs from these patient cases. 

Then they determined which patient in each pair should be prioritized, deferred, or sent to the emergency room. He then posed the same scenarios to three leading AI models – GPT-4, Google Gemini, and Anthropic’s Claude. The goal was to evaluate how often the AI models agreed with the human being. The result was that while all three models performed well on straightforward cases, in more complex or ambiguous cases, the AI models showed “concerning” variability, even contradicting themselves. 

The bottom line was that AI models did not always “behave” as expected because they are trained on biased datasets. While the data certainly matters, human feedback teaches a system how to respond, which perspectives to prioritize, and which behaviors to reinforce. The questions then become: who works with AI-generated responses, who handles testing and compliance, and who oversees process automation? Health insurers who use AI to automate care authorization decisions are facing lawsuits from patients who argue that the algorithms can unjustly deny care. As an example, UnitedHealthcare now has more than 1,000 AI applications – and is facing a 2023 class-action lawsuit alleging the company used flawed algorithms to deny claims. Despite this, an April 23, 2026 article claims that UnitedHealth Group is “on track to invest $1.5 billion in AI.” 

The murder of Brian Thompson, the former CEO of United Healthcare (the largest health insurance company in the U.S.), has brought the practice of healthcare insurers’ use of AI algorithms to deny patient coverage under intense scrutiny. In fact, according to The Regulatory Review, the use of unregulated coverage algorithms leads to delays in patient care, improper claim denials, and potentially deadly consequences for patients. Some have argued that the FDA has preexisting authority to regulate these coverage algorithms, although the agency has not yet taken action on the issue. 

In an attempt to control costs, some healthcare insurers have designed AI algorithms to determine whether a specific, provider-recommended course of treatment is medically necessary and whether the treatment qualifies for coverage under the patient’s insurance plan. After these algorithms were implemented with UnitedHealthcare, the company’s denial rate for post-op care more than doubled. Clearly, more robust oversight of these algorithms is necessary to ensure accuracy and fairness.   

Do Health Insurance Companies Use AI to Deny Claims?

Many people have no idea that their health insurance company may be using AI to deny claims. While companies are, in fact, doing just that, the reality is a bit more nuanced. Health insurance companies have implemented AI to review claims, process prior authorization requests, flag claims for further review, and determine what care is medically necessary. 

That said, AI rarely directly denies claims. This can be extremely confusing for patients. AI systems may screen or recommend denials, particularly for routine or high-volume claims. While human reviewers ostensibly make the final decisions, this human oversight is often minimal at worst and rushed at best.  

The higher number of claim denials can likely be traced to increased use of AI, despite numerous studies – as well as legal experts – warning that algorithm-driven decisions can lead to wrongful denials, even “blanket” denials. Patients are often unaware that AI was involved in a denial, and even the regulators say it can be difficult to determine how these determinations are made. 

Yet when medically necessary care is denied by AI, it is a serious issue. Some states – like California – have gone so far as to prohibit denying a healthcare claim based solely on AI, without actual human review. Clearly, the line between assistive technology and “de facto decision maker” has been blurred, resulting in more denials and growing legal and regulatory scrutiny. If you have had a medical claim denied, there is a real possibility that AI played some part in the decision, and that, as a result of this, context that is important to the decision could have been overlooked. 

How Can Insurance Companies Use AI to Deny Treatments?

Insurance companies can and do use AI in the process that leads to treatment denials; however, this practice is increasingly more regulated. AI is often used in the following ways:

• Determining whether recommended care is “medically necessary.”
• Approving or denying treatments before they are administered, as in prior authorization. 
• Processing and adjudicating claims

Practically speaking, this means AI could flag a claim for denial or approval, compare treatments against a medical insurer’s internal guidelines, and predict how long care should last. So, while AI can deny a specific treatment, it often does so indirectly, and, theoretically, rarely acts alone. While AI may recommend or automate decisions, a human reviewer is responsible for reviewing them. In reality, these human reviewers may be little more than a “rubber stamp” for an AI decision. 

Because some AI systems process claims so quickly, it may be doubtful that any meaningful human review was involved. AI algorithms often deny care like rehab services or nursing home stays, as well as necessary medical care. Since as many as 20 percent of in-network claims are denied on average, AI can accelerate the denial process. While the use of AI can result in faster decisions, lower administrative costs, and potentially more consistency, there is a very real risk of incorrect denials, lack of transparency, and fewer patients appealing these decisions. 

What Are the Specific Ways AI is Used to Deny Medical Treatments?

Insurance companies use AI at multiple points in the decision-making process, meaning AI can strongly influence a treatment denial. One of these points is considered “front-end gatekeeping” in the form of prior authorization screening. Before you can receive certain treatments, your insurer must approve them. An AI system can scan prior authorization requests from physicians and compare them to the company’s internal rules and guidelines. AI may flag these prior authorization requests as “approved,” “denied,” or “needs review.” If your treatment does not perfectly match your company’s predefined criteria, AI could flag it, potentially leading to a quick denial or minimal human review.  

AI can deny medical treatment by deeming it “not medically necessary.” This involves comparing your case to large datasets, applying standardized treatment pathways, and predicting what “should happen for your medical condition. Insurers use “medical necessity” to determine whether a treatment is appropriate for the condition, is supported by clinical guidelines, and is the most cost-effective option. AI is now heavily involved in answering these questions. If your case falls outside the AI algorithm’s “expected” patterns, it may suggest an alternative (likely cheaper) or shorter treatment or simply deny the treatment altogether. 

AI can affect how long you can receive a certain treatment through length-of-care prediction tools. Predictive models are used to determine how long your rehab should be following a specific surgery, how long you should stay at a skilled nursing facility, or the duration of home health care. If AI predicts a shorter recovery time than your actual recovery time, then your coverage could end early. Any additional care may be denied as “unnecessary.” This has been a major issue with Medicare Advantage patients and in lawsuits that involve post-acute care

AI could be responsible for a treatment denial due to an automated claims-processing system. Following your treatment, AI may review billing codes, identify mismatches or errors, and instantly apply the company’s specific rules. This means that a minor coding inconsistency that might have been caught by a human being can trigger a denial. Technical issues often override medical reality, and your claim might be rejected without a deeper, human review. 

AI may flag a claim as fraudulent, leading to a denial, even when it is not, in fact, fraudulent. A claim may be flagged as “unusual,” subjecting it to stricter scrutiny. Denial at this point is much more likely, even when the care or treatment is entirely valid. In short, when a claim is “different” from the dataset, it can work against you and result in a claim denial. In all these cases, AI decisions can break down in “real-world” care due to their one-size-fits-all logic. Algorithms rely on standardized rules, but you, as a patient, are far from “standard.” Rare conditions, complications, and unique medical histories often fail to fit into AI models. 

Do Insurance Companies Use AI to Deem a Treatment Experimental?

Insurers too often deem a treatment “experimental” or “investigational” to avoid approving it. Typically, when a treatment receives these labels, it is also expensive. When AI is used to determine whether a treatment is experimental or investigational, it will cross-check external clinical guidelines, FDA approval status and indications, and internal insurer coverage policies. A treatment that falls outside these approved uses or policy criteria may be flagged as experimental.  Some AI tools will look at published studies, clinical trial data, and the strength of the evidence. 

If there are limited studies, conflicting results, or usages that are not yet standardized, AI may flag the claim as “denied.” Further, even though off-label use of FDA-approved drugs is common, a treatment may be denied if the dosage is outside “standard” ranges or if it is prescribed for a different condition than labeled use. Unfortunately, the AI-driven classification of “experimental” can lag behind real-world medicine. Further, AI systems tend to favor large, long-term studies and guidelines with clear consensus. Many prescribed individualized or innovative treatments may not fall within AI guidelines.   

Why Would an Insurer’s Use of AI to Deny a Claim Be Problematic?

Insurers would likely say that AI makes claims decisions more quickly and more consistently. While this is true in part, when AI is used to drive or influence claim denials, the algorithms are largely geared toward “one-size-fits-all” medicine. While AI makes decisions based on standardized guidelines and historical data patterns, real patients have multiple conditions, complications, and unique responses to treatments. If you fail to fit the model, then you could face an unfair claim denial. 

AI decisions do not reflect actual medical judgment, even though, in theory, a qualified clinician makes the final call. All too often, the human review becomes nothing more than a rubber stamp. There can be a serious lack of transparency because the human on the other end of the denial may have no idea what criteria were applied, what data AI relied on, or precisely why the claim was denied. This makes it much more difficult to challenge a denial or provide the correct supporting evidence. While AI enables processing thousands of claims in a short time, these faster decisions too often mean less careful review and a greater risk of wrongful denials. 

In fact, in May 2026, the Medicaid and CHIP Payment and Access Commission (MEDPAC) recommended increased transparency into the AI-backed prior authorizations process. Specifically, MEDPAC recommended that a human with appropriate expertise review AI medical necessity denials in managed care plans. A person who understands a specific enrollee’s behavioral, medical or long-term care needs should determine medical necessity denials in fee-for-service Medicaid. CMS should provide guidance on how states can utilize regulatory authority to oversee health insurance company usage of automation in utilization management. And finally, Medicaid agencies should  require insurance companies to reveal how they use AI for authorization and coverage decisions.

Can You Fight an AI-Driven Health Insurance Denial in California?

The good news is that you can absolutely fight an AI-driven health insurance denial, and, in some cases, the AI factor can make the denial easier to legally attack. California law requires that coverage decisions be based on:

• Individual medical necessity
• A good-faith evaluation of the claim
• Accepted clinical standards

An insurer who relied too heavily on AI to the point where your doctor’s judgment was ignored, rigid one-size-fits-all criteria were used, or a meaningful human review was skipped has opened the door to a wrongful denial claim or a claim of bad faith. If your claim denial came very quickly (sometimes the same day), the explanation for the denial is vague or overly generic, or policy criteria are cited without discussing your specific condition, your claim denial likely had AI input. If your doctor says that the claim denial does not make “clinical sense,” then AI may have been responsible for the denial. 

Your first step is to file an internal appeal with your insurer, including a detailed letter from your treating physician, medical records that show why your treatment is necessary, and evidence showing that the criteria used were outdated, too narrow, or inconsistent with current medical practice. This forces your insurer to re-evaluate the decision, usually with a human reviewer. You have the right to ask who reviewed the claim, whether AI tools were used, and what guidelines were used. A lack of transparency can become an issue in your appeal. 

If your internal appeal is unsuccessful, you can seek an Independent Medical Review (IMR), which is one of your strongest tools in California. A neutral physician, rather than your insurer, will review your case. Many denials are overturned at this point in the process. However, whether you file an IMR or take legal action at this point may be based on your specific type of insurance plan. If you have a plan governed by ERISA, you must exhaust all your appeal options first. If you have a non-ERISA plan, you may take legal action instead of filing an IMR. We suggest that you speak to a knowledgeable insurance denial lawyer prior to making this decision. This attorney can also help determine whether your denial was wrongful, whether there was insurance bad faith, or whether California’s insurance regulations were violated. 

If it is found that your insurer relied on an automated system without real review or that the denial caused you harm, you may have a strong case. In fact, the use of AI can actually strengthen your position if you can show a lack of individualized review, an over-reliance on cost-driven systems, or if there are questions regarding compliance with medical standards. California law requires real medical judgment, not just algorithmic decisions. Health insurers in the state may not deny coverage based solely on AI or algorithms, and a licensed healthcare professional must review and approve any denial. 

If AI is used, it must rely on individual patient information rather than generic rules. Recent California legislation explicitly states that AI may not independently “deny, delay, or modify” care; rather, such a decision must come from a qualified human reviewer who must be “meaningfully involved” in the decision.  

If your insurer effectively lets AI make a decision, California utilization review laws may be violated, and a claim for wrongful denial or bad faith can be supported. Your position is strengthened in an appeal or IMR if your denial was largely made by AI. AI can assist but cannot make a final denial decision on its own in California, and if a real, qualified human review did not occur, the denial may be challenged. 

How Do Doctors Feel About Insurance Company AI Decisions?

Doctors tend to be skeptical and frustrated with the use of AI in medical decision-making, particularly in prior authorization cases. Doctors sometimes feel that AI-driven insurance decisions “second-guess” clinical judgment, delay necessary treatments, and force doctors to justify obvious care. This leads physicians to feel that they are fighting algorithms rather than treating patients. Doctors are trained to make individualized decisions based on a patient’s history, symptoms, and any real-time changes in condition. AI, on the other hand, relies on standardized rules, preset criteria, and data averages. 

And, in many cases, instead of reducing workload, AI has led to more denials requiring appeals, more documentation, and more prior authorization requests. This results in doctors reporting spending hours each week dealing with insurer requirements related to AI and algorithms. Doctors frequently say that they are unable to determine what criteria were used, why a treatment was rejected, or how to tailor an appeal as a result of AI denials. Doctors also worry that AI-driven denials can delay urgent care, interrupt ongoing treatment, and lead to adverse health outcomes. 

How Can AI Help Me Understand My Health Insurance Policy?

While you now know ways AI can go wrong as far as insurance denials are concerned, AI can be genuinely useful to you in many other ways, particularly when trying to understand your health insurance policy. When you use AI properly, it can turn a 60-page document full of legal jargon into something you can actually understand. AI does this by rewriting sections in plain language, summarizing long provisions, and explaining what a particular clause means for you. Next, AI can find the exact rules that apply to your specific situation, saving you from slogging through dozens of pages. 

AI can search your policy for specific topics, such as “weight loss drugs,” to determine whether they are covered. If you are facing a denial or pre-authorization issue, AI can highlight the applicable requirements, exceptions, and exclusions. AI can quickly identify your coverage limits, step-therapy requirements, and whether any experimental-treatment clauses exist. While healthcare policies describe costs in abstract terms, AI can translate them into real-life scenarios. For example, “If this procedure costs $17,000, you will pay about $X.” This can help you anticipate the financial impact of a treatment before it occurs. 

AI can compare your insurance policy with your physician’s recommendations, cross-referencing them to identify gaps in coverage. It can also flag whether prior authorization is required and what documentation you will need. It is important to know that AI is helpful in these situations, but not perfect. It may miss state-specific legal protections and cannot replace the judgment of a doctor or lawyer. For high-stakes issues, AI should be used only as a support tool, not as the final authority. 

In addition, please keep in mind that many AI programs use data from people to train their systems. As such, never put confidential information such as names, addresses, social security numbers, and more into AI tools. Also, if you have a paid subscription to an AI tool such as ChatGPT, Google’s Gemeni, Claude and others, you often have the option to specify in settings that you don’t want your information to train its system. With a subscription, you can usually toggle this option on to feel more confident that you have more privacy.

Can I Use AI to Find Better Health Insurance?

AI can be a powerful research tool that helps you find better insurance by narrowing your options and spotting key trade-offs before you choose a plan. AI can summarize multiple plans side by side, highlight key differences in deductibles, copays, and out-of-pocket maximums, and even point out policies with narrow networks or rigid referral rules. Policies often list costs in abstract terms that AI can translate into a specific scenario. For example, this low-premium plan costs more if you actually use it. 

This can help you avoid plans that seem inexpensive but will cost more in the long run. If you tell AI about your medications, ongoing conditions, preferred doctors, and risk tolerances, it can match a plan to your personal needs. When comparing plans, AI can scan plan documents and alert you to step-therapy rules, prior-authorization requirements, and limited networks, minimizing the risk of later claim denials. 

AI can help you find health insurance plans you may not have been aware of, exploring options across private insurers, HealthCare.gov plans, employer plans, and short-term or supplemental plans. While AI can be very helpful in choosing your health insurance plan, you should never blindly rely on it. In some cases, AI may lack real-time accuracy for provider networks or up-to-date plan availability and pricing. In addition, plans often change their formularies and in-network providers multiple times throughout each year.

Again, please keep privacy issues in mind. Never input confidential identifying information such as your name, address, email, phone number or social security number into a free AI tool.

Can AI Write an Appeal Letter for Me Following an Insurance Denial?

AI can often help you write a strong appeal letter following an insurance denial, turning a confusing situation into a clear, structured argument. AI can take your denial letter, your diagnosis and treatment, and your doctor’s recommendations and structure them into a persuasive appeal that includes a concise summary of your case, details why the denial is incorrect, and includes supporting medical reasoning. AI can incorporate the “right” language into your appeal so it sounds organized, credible, and harder to dismiss. 

Depending on the reason your treatment was denied, AI can directly address common denial reasons and detail and organize your supporting evidence. For Californians, a well-written appeal is powerful because it can qualify you for an IMR and force insurers to justify a denial based on medical standards. AI cannot replace your doctor’s medical opinion, guarantee approval, or access your policy without you providing it. You will still need to attach real documentation, double-check facts, and ensure the letter is fully customized to your unique situation.   

One resource that can help you write an appeal letter is Counterforce Health. This is a non-profit that helps individuals for free. 

Are There Privacy Concerns Regarding the Use of AI in Healthcare?

There are very real privacy concerns regarding the use of AI in healthcare. The technology may improve care and efficiency, but it relies on large volumes of sensitive personal data, such as medical histories, diagnoses, test results, insurance claims, and medications. This data is often shared across multiple systems once AI is involved. Healthcare data is a prime target for hackers because it can include financial data, detailed medical records, and identity information. 

AI increases the likelihood of a hack by connecting more systems, centralizing large datasets, and increasing data sharing between vendors. Patients may not know how their data is being used and who has access to it. While HIPAA protects certain health information, its protections mainly apply to healthcare providers and insurers. Some AI tools and apps fall outside HIPAA rules, and data shared with third-party vendors may not be covered. 

Since AI can analyze data patterns to predict health risks, flag high-cost patients, or influence insurance decisions, there is a risk of profiling and discrimination, which can lead to limited access to care or increased scrutiny for certain patients. So, while AI in healthcare can bring real benefits, it also comes with real privacy concerns.

How the Law Offices of Scott Glovsky Can Help

If you are confused about how AI may have influenced your claim denial or are wondering how AI can help you understand your policy to prepare an appeal, attorney Scott Glovsky can help. Scott has been fighting for justice on behalf of his clients for more than two decades. He understands how large insurance companies operate and knows how to counter their actions effectively. Scott and his legal team can work to help you get a denial reversed so you can receive the treatment your doctor believes is necessary for your health and your future. Contact the Law Offices of Scott Glovsky online or call 626-243-5598 today.