Comprehensive analysis of the risk-based classification system in regulating artificial intelligence applications within the healthcare sector and its implications

In June 2024, the EU adopted the Artificial Intelligence Act (AI Act), which is the world’s first detailed law to regulate AI development and use. This law divides AI applications into three groups based on their level of risk:

• Unacceptable Risk: AI systems that cause illegal or harmful effects on basic rights are banned. For example, AI that controls the thinking of vulnerable people, social scoring based on personal traits, and biometric identification in public places are banned unless used by law enforcement in strict cases.
• High Risk: AI systems that could affect people’s safety or basic rights, especially in medical devices, aviation, or critical infrastructure, are in this group. These systems need thorough testing, checking before being sold, registration in an EU database, continuous monitoring, openness about how they work, and ways for users to complain.
• Minimal or Limited Risk: AI systems with little risk have fewer rules. This helps innovation while keeping safety in mind.

In healthcare, AI used for diagnosis, treatment help, managing patients, and medical devices is usually considered high-risk because it can affect patient health and safety. For instance, AI in imaging devices that help doctors read scans or systems that help decide prescriptions are carefully controlled.

The law requires healthcare AI providers to check the risks many times, explain how AI systems work, and allow humans to control AI decisions to avoid unintended harm. This system aims to balance keeping patients safe, respecting rights, and supporting technical progress.

The Colorado Artificial Intelligence Act: Adopting a Risk-Based Approach in U.S. Healthcare

In the U.S., there is no single national AI law, but some states like Colorado are taking action. The Colorado Artificial Intelligence Act (CAIA), starting February 1, 2026, is the first full state-level AI law in the U.S. It uses a risk-based system like the EU and focuses on high-risk AI in areas like healthcare, education, jobs, and legal services.

Under CAIA, high-risk AI systems are those that make or affect important choices in services like healthcare. The goal is to stop AI from causing discrimination based on things like race, disability, or age. AI creators and users in healthcare must do the following:

• Give clear public details on what the AI is used for and its possible risks.
• Record where the data comes from, how the AI was tested, and ways to reduce unfair results.
• Tell the Colorado Attorney General if the AI causes or might cause bias or harm.
• Inform patients when AI is used in decisions about their healthcare, explain bad outcomes, and offer appeal options or ways to fix data.

CAIA mostly targets high-risk AI but does not apply to small businesses with fewer than 50 workers or AI systems regulated by federal laws like HIPAA. This helps smaller groups and those already following privacy rules.

For healthcare providers in Colorado, this means more responsibility to be open about AI and watch over AI systems carefully. The law’s focus on fairness fits well with existing healthcare rules.

Comparing Risk-Based Frameworks: Implications for U.S. Healthcare

Both the EU AI Act and Colorado’s CAIA use risk-based systems, but they differ in reach and enforcement. The EU law covers all member countries and focuses strongly on human rights, safety, and the environment. It requires thorough tests, human control, and registration especially for healthcare AI. It also supports innovation by helping startups test AI in real settings.

Colorado’s law is smaller in scale but also requires documentation, risk management, and protection against unfair AI in healthcare. It follows standards like the National Institute of Standards and Technology’s AI Risk Management Framework (NIST AI RMF) to set clear rules for reducing risks.

Healthcare leaders in the U.S. should know that AI systems affecting patient care will face more rules from both laws. Even AI devices approved by the FDA may need to meet extra state rules about fairness, openness, and informing patients.

Application of AI in Healthcare: Workflow Automation and Phone Operations

AI is changing how healthcare offices work at many levels, including front-desk tasks. Companies like Simbo AI provide AI-powered phone systems that can handle lots of patient calls, book appointments, answer common questions, and direct calls using natural language processing (NLP).

Using AI for phone systems helps healthcare offices by lowering staff work, reducing missed calls, and giving patients better access to services. AI can direct calls correctly, send automated reminders, and gather patient information while following privacy laws.

These AI phone systems usually have low risk since they do not make medical decisions but support administrative tasks. Still, it is important to be clear about AI use and keep data safe. Staff must watch the system and quickly pass complicated or emergency calls to humans.

Using AI automation fits with rules that say AI in healthcare should not risk patient safety or rights, as written in EU and U.S. laws. Providers should check that AI phone systems follow these rules and their own policies for patient communication.

Challenges and Considerations for Healthcare Leaders

• Compliance Costs and Resource Allocation: Meeting rules for documentation, testing, and reporting can be hard and costly, especially for small offices. Planning and possibly using outside experts can help.
• Human Oversight: Laws say AI should not replace human decisions in important cases. It is important to have clear ways for humans to check and control AI to avoid mistakes.
• Data Security and Privacy: AI uses large sets of data, including sensitive health info. Offices must protect this data and follow laws like HIPAA.
• Interoperability and System Integration: Adding AI tools to workflows needs systems that work well together and staff training to get the best results.
• Transparency to Patients: Regulations and ethics require telling patients about AI use in their care or communication. Clear explanations help keep trust and allow informed consent when needed.

Implications for Medical Practice Administrators, Owners, and IT Managers

Medical leaders in the U.S. should keep track of changing AI laws, including state rules like CAIA and future federal laws, to guide AI use. Important steps include:

• Risk Assessment: Regularly check AI tools by their risk level. High-risk ones need strict testing and ongoing reviews; lower-risk tools need less oversight.
• Documentation: Keep records of AI design, training data, tests, risk reduction methods, and user feedback.
• Staff Training and Oversight: Make sure staff know how AI works, its limits, and when to question or override AI results.
• Consumer Notifications: Set up ways to inform patients when AI affects decisions, to keep transparency and protect patient rights.
• Vendor Due Diligence: When choosing AI providers, check that they follow laws and standards about privacy, fairness, and transparency.
• Collaboration with Legal and Compliance Experts: Work with experts who know AI rules to stay updated and follow best practices.

Future Outlook and Regulatory Trends Affecting Healthcare AI

AI rules in the U.S. and elsewhere are changing fast. After Colorado, other states may make similar laws for high-risk AI, especially in healthcare. Federal agencies like the FDA are also updating their guidance on AI and machine learning in medical devices.

The focus on human-centered, risk-based rules will likely continue to balance new technology with safety and rights. Healthcare organizations will need strong management of AI to stay legal and use AI well to improve care and office work.

By understanding risk-based systems and managing AI carefully, healthcare leaders can handle compliance work and keep patients safe without stopping the advantages AI can bring to care and administration.