Challenges and best practices for managing regulatory complexity caused by varied AI disclosure requirements affecting healthcare providers using generative AI tools

Several states have passed or plan to pass laws that require businesses, including healthcare providers, to tell people when they use generative AI tools. These laws aim to make things clearer and stop people from being tricked when they don’t know they are dealing with machines instead of humans.

The Utah Artificial Intelligence Policy Act (UAIPA), starting May 1, 2024, is one of the first big laws that say healthcare workers and others licensed must clearly say if they use AI when talking to people. Other companies must tell people if they ask or interact with AI-generated content directly.

California is also making rules. The California AI Transparency Act (SB 942), starting January 1, 2026, focuses on AI companies that have over one million users in California each month. They must provide free tools to detect AI content and must say when content is made by AI. Companies that don’t follow this can be fined $5,000 for each time they break the law. This shows how states are watching closely to stop misinformation, especially in areas like healthcare where decisions are personal.

Colorado’s AI Act (CAIA), starting February 1, 2026, aims to stop discrimination by AI in healthcare and jobs. Healthcare providers must study how their AI systems affect people before using them and every year after. They must be open about when AI is used to make big decisions, like scheduling, billing, or treatment choices, and tell patients about it.

Other states such as Alabama, Hawaii, Illinois, Maine, and Massachusetts are working on bills that focus on clear AI disclosures to stop unfair or misleading acts, especially when AI pretends to be human without telling people.

At the national level, the AI Disclosure Act of 2023, which wanted to force warnings on AI-generated content and have the Federal Trade Commission enforce them, is currently on hold. Meanwhile, a proposed 2025 bill by House Republicans could stop state AI laws for up to ten years. This may cause issues with making rules consistent and protecting consumers.

Challenges for Healthcare Providers Using Generative AI

1. Diverse and Evolving State Requirements

Healthcare providers working in many states face tricky challenges. Each state has different laws about AI disclosure. Big medical groups or healthcare networks must figure out which rules matter and ensure they tell patients about AI in ways that match each state’s laws.

For example, Utah wants AI use to be stated clearly before starting. California wants tools available to spot AI content and constant openness if many users are involved. Colorado focuses on stopping bias and requires risk studies and public notices that go beyond just saying AI is used. Keeping up with these changes means watching laws carefully and being ready to change policies.

2. Complexity of Healthcare-specific AI Use Cases

AI is used in many healthcare jobs, like booking patients, billing, giving diagnostic help, and planning treatments. Colorado’s law points out risks like voice recognition problems for people with accents or those who do not speak English well. Biased AI could hurt some groups more.

Because of this, providers must not just disclose AI use. They also must make sure AI does not unfairly treat people based on race, language, age, or disabilities. This needs studies before and during AI use, and ongoing checks to keep care fair.

3. Documentation and Transparency Obligations

Laws ask for a lot of paperwork. According to Charles S. Gass and Hailey Adler of Foley & Lardner LLP, healthcare providers must keep clear records about how AI is used, what kind of data trains it, any known biases, risks, and the steps taken to reduce these risks. They should share this information on their websites and tell patients before using AI decisions.

For busy clinics, making and keeping this information is hard. They need enough staff, training, and good technology to track and report how well AI systems work.

4. Legal and Contractual Risks

Healthcare providers not only use AI but also buy it from developers. They must carefully check and argue about contracts with AI makers to be clear on who is responsible if something goes wrong, how data is used, and help with following rules. Charles S. Gass says sharing risks and open agreements are important to avoid surprises later.

If these points are not handled early, there could be gaps in following rules, especially if AI makers change their data or software which might cause new problems after the AI is already in use.

Best Practices for Managing AI Disclosure Requirements in Healthcare

1. Implement a Robust AI Governance Program

Healthcare providers should create a clear system for handling AI use. This system should:

• Find out which AI tools are “high-risk” or controlled by laws.
• Require detailed studies before using AI that explain why the AI is used, its benefits, known biases, and risks, and ways to reduce problems.
• Keep checking and rechecking the AI regularly, especially after updates or changes in data.
• Use a calendar to track laws and when to do rechecks or disclosures.

A central team dedicated to AI governance can help keep rules uniform and lower chances of breaking laws.

2. Prioritize Transparent and Proactive Disclosure

Even if laws don’t say to tell people about AI upfront, healthcare leaders should still be clear when AI affects healthcare choices. Ways to do this include:

• Adding statements on websites about AI use and efforts to prevent bias.
• Saying before appointments, billing, or clinical decisions that AI is involved.
• Providing simple explanations and options for patients to appeal if AI causes a bad outcome.

Being open helps build patient trust and stops accusations of unfair or misleading behavior.

3. Invest in Staff Training and Education

Everyone who works with patients and might use AI—front desk, schedulers, doctors, IT managers—should get training about AI tools, disclosure rules, and risk management basics. Knowing AI’s limits and rules helps talk clearly with patients and lowers errors.

Training should cover:

• How to notice when AI is part of a patient interaction.
• How to clearly tell patients about AI and answer their questions.
• How to report AI problems or biases to the right people.

4. Conduct Regular AI Audits and Risk Assessments

Following Colorado’s law, healthcare providers should check AI systems regularly to find and fix any bias or mistakes. Checks should look at:

• Whether diagnostic AI works well for all patient groups.
• Whether scheduling AI understands different accents and languages.
• How well the AI performs and what error rates it has over time.

These checks help prove that providers follow rules and help improve AI over time.

5. Review and Manage Vendor Relationships Carefully

Healthcare groups depend on outside AI providers, so they should:

• Work carefully on contracts about who follows rules, shares data, and helps with impact studies.
• Ask vendors to share details about training data, known biases, and any updates that might affect AI.
• Make sure vendors help with public disclosure or patient notices if needed.

Working well with vendors helps meet rules on time and lowers legal risks.

AI and Workflow Automations: Meeting Compliance While Enhancing Efficiency

Generative AI is often used in healthcare offices to automate answering phones, scheduling, prescription refills, and billing questions. These tools can reduce work and improve patient service. But AI disclosure rules must be part of these workflows to avoid breaking laws and upsetting patients.

Ways providers can match AI automation with disclosure rules include:

• Automated AI Disclosure Prompts: AI phone systems, such as those from Simbo AI, can be set to tell patients at the start of calls that they are talking with an AI assistant. This meets Utah’s UAIPA and likely future rules in many states.
• Recording and Logging AI Interactions: Automation tools should save call records and when disclosures are made during AI chats. This helps providers prove they follow rules during checks.
• Patient Choice and Escalation Options: Automation should let patients ask for a human helper if they want or if the AI can’t answer properly. This supports openness and cuts down frustration with unclear AI handling.
• Integration with Compliance Dashboards: AI front-office tools can link to software that tracks disclosures, checks AI performance, and alerts managers about rule changes or needed actions.
• Accessibility and Bias Mitigation Mechanisms: Automated systems should include features that understand different languages, accents, or disabilities, based on laws like Colorado’s AI Act. Making sure services are fair and easy to use helps follow rules and keep patients happy.

Healthcare providers should see AI automation not just as a time-saving tool, but as part of a system designed to meet legal and ethical needs.

Final Observations for Healthcare Providers in the United States

Medical practice managers, owners, and IT staff working with AI tools face the challenge of dealing with many different state AI disclosure laws that vary in what they require and when. Using strong governance plans that include full risk studies, public disclosures, staff training, audits, and vendor checks will lower risks and build trust with patients.

Being open about AI in patient communications and medical decisions follows new state laws like Utah’s UAIPA, California’s SB 942, and Colorado’s AI Act. Since federal laws are still uncertain, providers have to keep watching state laws to stay in line.

Adding AI to office tasks like answering phones can meet rules while improving how clinics run—if disclosure steps are included and AI performance is watched carefully.

Watching AI governance closely will become an important part of running healthcare as AI tools get better and more common. Providers who manage openness and risks well will serve patients better and protect their organizations from legal problems.