AI is increasingly being adopted into UR to streamline review processes, improve efficiency, and potentially reduce costs. AI tools and agents can automate tasks, analyze patterns, perform deep research, and provide decision support, but they may also introduce potential risks like over-reliance and algorithmic bias. NAIRO has observed a dramatic increase in states creating bills around the use of AI in UR. We have noticed that states have taken a wide-ranging approach to regulating AI into their respective UR regulations, but all agree that some form of human oversight is needed. For example, these bills are currently in process:

• Illinois House Bill (HB) 35 (2025) proposes to create the Artificial Intelligence Systems Use in Health Insurance Act, which provides the Department of Insurance with regulatory oversight of health insurance coverage including oversight of the use of AI systems or predictive models to make or support adverse consumer outcomes.
• Maryland HB 820 (2025) Requires that certain carriers, pharmacy benefits managers, and private review agents ensure that AI, algorithm, or other software tools are used in a certain manner when used for conducting UR.
• New York Assembly Bill 8556 (2025-2026) prescribes requirements and safeguards for the use of an AI, algorithm, or other software tool for the purpose of UR for health and accident insurance.
• Rhode Island Senate Bill (SB) 13 (2025-2026) - Use of Artificial Intelligence by Health Insurers, which promotes transparency and accountability in the use of AI by health insurers to manage coverage and claims.
• Tennessee SB 1261 (2025-2026) Insurance Agents and Policies, which imposes requirements for health insurance issuers using AI, algorithms, or other software for UR or utilization management functions.

NAIRO believes there are important benefits in incorporating AI in UR, including increased efficiency and speed. For example, AI can automate routine tasks like data extraction, prior authorization requests, and initial case reviews, freeing up human reviewers for more complex cases. It can also enhance accuracy and consistency. Foundation AI models such as Google’s Gemini 2.5, Open AI’s GPT 4.5 and others now have the context and reasoning capabilities. They can analyze large datasets and identify hidden patterns that could easily be missed by humans, leading to more accurate and consistent decisions. It would result in improved decision support by providing evidence-based recommendations and highlighting relevant clinical information to facilitate human reviewers in making informed decisions.

Using AI in the UR process can be cost efficient by reducing administrative burden and potentially decreasing the number of denials. By analyzing data and flagging potential issues, AI can offer improved compliance with regulations and guidelines.

Currently AI combined with digital technologies is being used in the UR process to manage the following tasks:

• Automating Prior Authorization: AI can automate the process of submitting and reviewing prior authorization requests, streamlining the process, and reducing delays.
• Identifying Medical Necessity: AI can analyze patient data to determine if a requested service is medically necessary, based on clinical guidelines and evidence.
• Optimizing Case Prioritization: AI can prioritize cases based on factors like clinical complexity, potential for cost savings, and risk of denial.
• Detecting Fraud and Abuse: AI can be used to identify claims fraud and abuse by analyzing claims data to identify patterns of fraud and abuse, helping to prevent financial losses.

Despite all of the obvious significant advantages of incorporating AI into the UR process, NAIRO understands potential risks and additional considerations remain. Over-automation could lead to a decline in human expertise and decision-making skills if not managed carefully. AI algorithms can perpetuate existing biases in healthcare data, potentially leading to unfair or discriminatory outcomes. Some AI systems, particularly black-box models, may lack transparency, making it difficult to understand how decisions are made and potentially leading to liability issues. Lastly, AI systems rely on substantial amounts of patient data, raising real concerns about data privacy and security.

The use of AI in healthcare is still relatively new, and regulations as well as AI capabilities continue to evolve, creating uncertainty for all UR stakeholders. NAIRO will continue to track these regulatory developments and work with industry stakeholders to ensure AI is introduced appropriately and used responsibly to protect the integrity of the UR process. Overall, AI offers significant potential for transforming utilization reviews, but it is crucial to implement it responsibly and ethically, with appropriate human oversight and safeguards in place.