States move toward a patchwork of rules for medical AI

The rise of state-level rules

As artificial intelligence becomes more integrated into the healthcare sector, the lack of a single national standard is creating a complex environment for providers. While federal agencies oversee some specific uses of medical AI, there is currently no comprehensive federal framework to govern the technology broadly. This vacuum has allowed individual states to begin crafting their own unique sets of rules.

The momentum toward state-level regulation is already significant and accelerating. According to Manatt Health, more than 280 AI bills affecting health care were introduced in state legislatures during the first half of 2026. This surge suggests that the shift toward localized oversight is becoming a permanent fixture of the American healthcare landscape.

By the end of July, 33 of these bills had already been signed into law across 22 different states. Brian Anderson, CEO of the Coalition for Health AI, told Newsweek that this trend is well underway. He noted that healthcare organizations should prepare for a patchwork of different regulatory frameworks that will define the next one to two years.

High-risk and agentic systems

Hospitals are beginning to explore the use of more autonomous systems, often referred to as "agentic" systems. These are AI models designed to perform tasks traditionally handled by human clinicians rather than just providing information. Because these systems can act independently, they raise significant concerns for health systems regarding safety and liability in high-risk scenarios.

Regulators are currently grappling with how to define the threshold for acceptable performance in these tools. Anderson raised questions about what level of accuracy is sufficient for a sepsis algorithm—a tool used to identify a life-threatening infection—before it can be trusted in a clinical setting. He questioned whether 95 percent, 80 percent, or even 70 percent accuracy would be considered safe enough by different state boards.

The difficulty lies in the fact that there is no consensus on these benchmarks yet. As AI moves closer to performing active clinical tasks, the lack of clear standards makes it harder for hospitals to know which tools they can safely deploy. This uncertainty is a primary concern for administrators who must balance innovation with patient safety.

The Utah prescription pilot

Utah is currently testing a specific approach to AI regulation through a pilot program involving Doctronic. This technology is designed to assist with the renewal of existing prescriptions, which is a common hurdle for patients who cannot easily see a doctor. Unlike a simple refill, a renewal typically requires a licensed prescriber to authorize the medication, a step pharmacists usually cannot perform alone.

The current pilot is in an initial phase where a licensed medical practitioner must authorize every single request made by the AI. This setup aims to solve access problems in areas facing provider shortages while maintaining a human check on the technology. It serves as a practical example of how states might begin to integrate AI into routine medical workflows.

However, the program has already faced scrutiny from the Utah Medical Licensing Board. In April, the board recommended suspending the program pending further discussion because they believe renewals require a full clinical reassessment. They expressed concerns that relying on AI for these tasks could lead to patient safety issues if the technology misses underlying health changes.

Risks in low-risk tasks

Even tasks that are considered relatively low-risk, such as medication renewals, carry inherent dangers in a medical context. Anderson pointed out that a patient's blood levels or laboratory values could be elevated or concerning in ways the AI might not immediately flag. In such cases, a human provider might need to adjust a dosage or stop a medication entirely to prevent harm.

Because these clinical nuances are so specific to the individual patient, there is a fear that automated systems could overlook critical data points. If an AI system handles a renewal without a human reviewing the most recent lab results, it could lead to adverse reactions. This highlights why even the most basic AI applications in medicine require careful oversight and clear safety protocols.

The goal of the current testing is to determine if these risks can be mitigated through human-in-the-loop systems. By requiring a practitioner to sign off on every AI-generated renewal, the pilot attempts to combine the efficiency of technology with the safety of human judgment. The success of such models will likely dictate how other states choose to regulate similar tools.

The challenge of national scale

The move toward 50 different state frameworks creates a significant logistical hurdle for technology companies and health systems. If a company has to jump through 50 different hoops to operate nationally, it may become economically unfeasible. Anderson warned that companies might decide that smaller states are simply not worth the effort of navigating unique local regulations.

This fragmentation can slow down the adoption of helpful technologies and create a lopsided landscape where only large systems can afford to comply. To combat this, the Coalition for Health AI is working with various groups and state governments to encourage greater consistency. They recognize that a patchwork of rules will naturally create friction and delay the deployment of new tools.

Ultimately, many advocates hope for a broader federal framework to provide a baseline of guidance for the entire country. Anderson expressed his hope that an overarching set of federal rules would eventually make the system easier to navigate. Until that happens, the next few years will be defined by a complex, state-by-state evolution of medical AI policy.