Healthcare & Technology

Who’s Responsible When AI Gets It Wrong? The Legal Gap in AI-Driven Healthcare

By Catalina Troy, YouthCare 10/25/2026 Connect

Artificial intelligence is becoming an increasingly powerful and common tool in healthcare, performing tasks such as analyzing medical records, assisting with diagnoses, and helping insurance companies process claims. As these systems become more involved in important medical decisions, an important question is created: who should be held responsible when AI makes a mistake? While AI has large potential to improve healthcare efficiency and accessibility, existing laws have not fully addressed the serious risks of allowing algorithms to influence a patient’s care. Healthcare policies must change to require meaningful human insight, greater transparency, and clearer legal accountability so that technological progress doesn’t come at the expense of patient safety or health.

When Technology Progresses Faster Than Regulation

Traditionally, healthcare regulation has focused on the responsibilities of different medical professionals, hospitals, and manufacturers of medical equipment and devices. AI introduces a new challenge because software can generate recommendations that influence decisions that once were made primarily by humans.

According to the Congressional Research Service, healthcare AI applications generally fall into three different categories, which include diagnosis and treatment, patient engagement, and administrative operations. Federal agencies, including the Food and Drug Administration, have developed oversight mechanisms for certain AI technologies, but many questions involving privacy, bias, and liability remain (Congressional Research Service).

The legal challenge is not simply whether AI should be permitted in healthcare. It is how existing protections can be adapted to these new technologies capable of influencing medical decisions without independently understanding their consequences.

Human Oversight: Who Makes the Final Decision?

One of the most significant areas of healthcare policy reform concerns insurance decisions. Insurers have increasingly used AI to assist with prior authorization, which is a process requiring approval before certain medical treatments or services receive coverage.

Although automation may accelerate administrative reviews, it also raises concerns when algorithms influence whether patients receive their necessary care.

In 2026, Washington enacted legislation prohibiting health insurers from relying solely on AI to deny, limit, or delay services through prior authorization. The law also requires appropriately qualified healthcare professionals to make adverse determinations, or decisions that negatively affect coverage (Silverboard et al.). This legislation represents an important shift in healthcare regulation. Rather than prohibiting AI entirely, it establishes limits on the authority algorithms may exercise.

However, requiring a human reviewer is only meaningful if that person independently evaluates the patient’s circumstances. A reviewer who automatically accepts an algorithm’s recommendation offers little protection against possible errors. Future policies should therefore require documented clinical reasoning and opportunities to challenge AI-assisted decisions to ensure that human oversight is more than just a procedural formality.

Transparency and the Patient’s Right to Know

Patients have traditionally been asked to provide informed consent before certain medical procedures, which means understanding relevant information about a treatment before voluntarily agreeing to it. As AI becomes more involved in diagnosis and treatment, patients may increasingly need to understand how these technologies may influence their care.

Texas has already taken steps in this direction. Under its Responsible Artificial Intelligence Governance Act, licensed healthcare practitioners must provide patients with written disclosure when AI is used in diagnosis or treatment, subject to special timing provisions for emergencies (Cohen and Hodge). Such requirements recognize that transparency is becoming part of responsible medical practice.

Yet, disclosure alone cannot guarantee understanding. Simply informing patients that AI was used provides limited value if they cannot determine what role the technology played. In the future, policies should distinguish between AI that performs routine administrative tasks and AI that substantially influences clinical recommendations. Patients should receive clear explanations when AI affects their treatment, rather than being overwhelmed with technical terminology that they may not know of.

The Accountability Gap in AI-Driven Medicine

Even with human oversight and disclosure, another difficult question remains: what happens when an AI recommendation contributes to patient harm? Consider a hypothetical patient whose medical scan is incorrectly classified by an AI diagnostic system. If the physician relies on that result and treatment is delayed, responsibility may involve the clinician, hospital, software developer, or multiple parties.

This uncertainty is already attracting attention from legal experts. Stanford professor Michelle Mello explains that healthcare AI liability is currently shaped by a combination of state laws, contracts, and professional judgement. Developers may limit their liability through contracts, potentially leaving healthcare providers responsible for problems involving technology they did not create (Mello).

The problem is not that AI should automatically be treated as more dangerous than traditional medical tools. Rather, its ability to reproduce mistakes across several patients makes consistent evaluation especially important. Future regulations may need to establish clearer responsibilities for developers and healthcare organizations, including testing systems before use, monitoring performance, and documenting how AI recommendations contribute to clinical decisions.

Protecting the People Behind the Data

Legal discussions about artificial intelligence can easily become focused on software developers, insurers, and regulatory agencies. Yet, the consequences of these policies are ultimately experienced by patients.

Imagine an older adult waiting for approval for essential treatment, or a family trying to understand why an insurance request was denied. If an algorithm contributes to that decision, patients deserve more than an unexplained rejection or a complicated appeals process.

AI systems may improve efficiency, but efficiency should not become the primary measure of whether or not healthcare is successful. The laws appearing across several American states suggest that technology can support medical professionals, but its influence must remain understandable, reviewable, and accountable. Achieving this balance will require cooperation among lawmakers, healthcare organizations, clinicians, and technology developers.

As artificial intelligence integrates more into medicine, the purpose of healthcare regulation must remain unchanged. Progress should be measured not only by what technology can accomplish, but also by how effectively it protects the people who may depend on it.