High-risk medical AI systems are AI tools used in important medical decisions or devices that affect patient health and safety. Examples include AI for early diagnosis like sepsis detection or breast cancer screening, robot-assisted surgery, personalized treatment plans, and automating clinical notes. These tools help healthcare workers but must be carefully controlled because mistakes could seriously harm patients. This is why rules for these systems are very important.
In the United States, government rules watch over AI used in healthcare. These include FDA rules for medical devices and HIPAA for protecting patient data. Unlike the European Union, which has a clear Artificial Intelligence Act from August 2024, the U.S. uses several agencies like the Food and Drug Administration (FDA), Centers for Medicare and Medicaid Services (CMS), and the Federal Trade Commission (FTC) to manage safety, fairness, and effectiveness.
The European AI Act divides AI systems into four risk levels: unacceptable, high, transparency, and minimal or no risk. It sets strong rules for high-risk AI, including those used in medicine. The U.S. can look at this act as an example for future standards.
High-risk AI systems must be tested carefully before use and throughout their life. This means checking the quality of data, how well the AI works, and possible dangers. The FDA’s Digital Health Center of Excellence gives advice on how AI software used as a medical device should prove it is safe and effective before being allowed on the market.
Because AI can change and learn over time, it is important to keep watching how it works after it starts being used. Hospitals must report any problems and update the AI to avoid new risks. If AI causes harm and rules were not followed, hospitals or companies could be held legally responsible.
AI depends on good patient data from electronic health records, images, tests, and more. Protecting this sensitive data is required by HIPAA laws in the U.S. This means using encryption, controlling access, removing identifying details when possible, and keeping records of data use. These steps help make sure private health details are safe while training and using AI.
The European Health Data Space (EHDS) shows how health data can be used carefully and ethically for AI. Although it is a European program, U.S. healthcare groups can learn from it how to develop AI while protecting patient privacy.
Being open about how AI works builds trust. Healthcare providers should explain how AI makes decisions. This includes showing information about the data used to train the AI, how algorithms work, and limits of the system.
The U.S. is still making clear rules on AI openness. However, its rules follow some ideas similar to Europe’s AI Act. Developers must keep logs of what the AI does, publish documents about it, and let users know when AI is being used. This helps doctors understand AI results and hold the system accountable.
AI tools must work under real human control, especially in situations where mistakes can hurt patients. Humans should check AI advice before acting, be able to step in if needed, and prevent full automation in critical choices.
The European AI Act requires high-risk AI to have human governance to avoid errors. In the U.S., the FDA also supports models where AI helps clinicians but decisions are still overseen by people.
One challenge is deciding who is responsible if AI causes harm. Europe’s updated Product Liability Directive treats AI software like a product under no-fault rules. This means manufacturers can be held responsible for problems caused by AI faults.
The U.S. does not have a nationwide law like this yet. Still, courts are facing more cases about AI mistakes in healthcare. Medical leaders should watch legal changes and manage risks carefully. Keeping detailed records of AI use helps protect against legal claims.
Using AI in medical office tasks helps handle more patients, cut costs, and improve service. AI can automate answering phones, scheduling appointments, checking in patients, and billing. This lets staff spend more time with patients.
Some companies, like Simbo AI, make AI phone systems just for healthcare. These systems answer patient questions all day and night, route calls automatically, schedule visits, and provide important information. This reduces waiting, cuts mistakes in data entry, and helps patients.
Even with automation, rules must still be followed:
AI also helps with clinical notes by transcribing conversations, updating electronic health records, and making reports. This saves doctors’ time and improves accuracy. These AI tools must fit well with existing technology and follow rules for handling clinical data.
Some U.S. groups look at international models like the European AICare@EU project, which tries to address technical, legal, and cultural barriers to AI in medicine. Even though it focuses on Europe, its ideas can help inform U.S. efforts.
AI rules in U.S. healthcare are still changing and take ideas from plans like the European AI Act and the European Health Data Space. Medical leaders should:
AI rules and use in healthcare are always changing. For U.S. medical managers and IT staff, staying up to date with global standards and new U.S. regulations is very important. This helps bring in safe and reliable AI that supports patient care and follows the law.
AI improves healthcare by enhancing resource allocation, reducing costs, automating administrative tasks, improving diagnostic accuracy, enabling personalized treatments, and accelerating drug development, leading to more effective, accessible, and economically sustainable care.
AI automates and streamlines medical scribing by accurately transcribing physician-patient interactions, reducing documentation time, minimizing errors, and allowing healthcare providers to focus more on patient care and clinical decision-making.
Challenges include securing high-quality health data, legal and regulatory barriers, technical integration with clinical workflows, ensuring safety and trustworthiness, sustainable financing, overcoming organizational resistance, and managing ethical and social concerns.
The AI Act establishes requirements for high-risk AI systems in medicine, such as risk mitigation, data quality, transparency, and human oversight, aiming to ensure safe, trustworthy, and responsible AI development and deployment across the EU.
EHDS enables secure secondary use of electronic health data for research and AI algorithm training, fostering innovation while ensuring data protection, fairness, patient control, and equitable AI applications in healthcare across the EU.
The Directive classifies software including AI as a product, applying no-fault liability on manufacturers and ensuring victims can claim compensation for harm caused by defective AI products, enhancing patient safety and legal clarity.
Examples include early detection of sepsis in ICU using predictive algorithms, AI-powered breast cancer detection in mammography surpassing human accuracy, and AI optimizing patient scheduling and workflow automation.
Initiatives like AICare@EU focus on overcoming barriers to AI deployment, alongside funding calls (EU4Health), the SHAIPED project for AI model validation using EHDS data, and international cooperation with WHO, OECD, G7, and G20 for policy alignment.
AI accelerates drug discovery by identifying targets, optimizes drug design and dosing, assists clinical trials through patient stratification and simulations, enhances manufacturing quality control, and streamlines regulatory submissions and safety monitoring.
Trust is essential for acceptance and adoption of AI; it is fostered through transparent AI systems, clear regulations (AI Act), data protection measures (GDPR, EHDS), robust safety testing, human oversight, and effective legal frameworks protecting patients and providers.
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