High-risk AI systems are those that, if they fail or do not work correctly, could directly harm patient safety. They might affect decisions about diagnosis or treatment or change important regulatory processes. Examples include AI that reads medical images, helps find serious diseases early like sepsis or cancer, or supports creating personalized treatment plans. These AI tools are often part of medical devices or electronic health records (EHR) systems.
The European Union has created a law called the Artificial Intelligence Act (AI Act), which started in August 2024. This law sorts AI systems into categories like unacceptable, high-risk, limited risk, and minimal risk. It makes strong rules for high-risk AI such as those used in medical devices. The United States does not have one main federal AI law yet. Instead, its rules are changing, guided by agencies like the Food and Drug Administration (FDA) and influenced by international organizations like the European Medicines Agency (EMA) and the Council for International Organizations of Medical Sciences (CIOMS).
Patient safety is the most important concern when using AI in healthcare. High-risk AI tools have to be tested carefully to make sure they are accurate, reliable, and strong. In January 2025, the FDA gave new guidance about AI used with drugs and biological products. This guidance uses a risk-based approach like the European AI Act. It calls for ongoing monitoring and managing risks.
For example, if an AI predicts early signs of sepsis or reads mammograms, it must be trained on high-quality data that includes different kinds of patients. This helps reduce bias and avoid harmful mistakes. The CIOMS Draft Report says AI needs continuous checking with real-world data to detect “model drift.” Model drift happens when AI becomes less accurate over time because things in clinical settings change.
Another part of safety is to reduce algorithmic bias. Bias can cause unfair care or discrimination. Both EU and U.S. rules say training data must be checked to stop bias. Healthcare providers should ask for transparency about the data used and confirm that AI makers have checked for bias carefully.
Legal responsibility is very important as AI systems get used more in medicine. In the EU, new laws like the updated Product Liability Directive treat AI software as a product. This means manufacturers might have to pay for harm caused by their AI without needing to prove fault. This helps victims get compensation and makes developers focus on safety from the start.
In the U.S., the legal rules are less clear but are changing. The FDA helps approve AI software, and both developers and clinical users share responsibility. Medical administrators and healthcare groups need to review contracts with AI vendors carefully. These contracts should cover liability, warranties, and federal rules compliance.
Risk is managed by requiring different forms of human oversight. These include “human in the loop,” “human on the loop,” and “human in command.” The level of supervision matches how much harm AI errors could cause. This ensures humans keep control and can override AI when needed. The final clinical judgment rests with medical staff.
Human oversight is an important rule for using AI safely in healthcare. Although AI can work faster and spot patterns humans might miss, it can still make mistakes. Therefore, AI software for high-risk uses must be designed to support human checks.
The European AI Act demands AI be made so healthcare workers can monitor, understand, and override its decisions. In the U.S., the CIOMS Draft Report and FDA guidelines encourage continuous human involvement during AI use.
Healthcare organizations should train their staff to understand AI, its limits, and why it is important to watch it carefully. Medical administrators should think about how human oversight fits into daily work. For example, AI’s diagnostic advice should be shown next to clinical notes, but the physician makes the final call. IT managers must set up tools to log AI outputs, user actions, and errors to help with reviews and compliance.
The level of oversight should depend on the type of task. AI used for routine tasks may need less supervision than AI helping with very sensitive decisions like end-of-life care or drug safety.
Transparency helps build trust among doctors, patients, and regulators. Companies that make high-risk AI must provide clear instructions. These include what the AI can do, how well it works, its limits, and possible risks. The European AI Act requires this clarity to support safe and responsible use.
In the U.S., regulators want detailed records about the AI model’s design, data sources, how decisions are made, and how humans interact with AI. The CIOMS Draft Report supports this transparency for auditing, investigating errors, and explaining AI decisions.
Data governance is part of transparency. It ensures patient data is handled ethically and kept safe. In the U.S., following HIPAA rules protects patient privacy when AI uses and learns from data. Healthcare groups must check that AI vendors handle data properly by removing personal identifiers, minimizing data use, and storing data securely.
It is also important to check data quality during care. Bad or incomplete data can cause AI to make wrong predictions. IT managers should set up checks according to vendor rules, especially for AI systems that learn or change based on new data over time.
AI is also changing front-office tasks in hospitals and medical offices. AI-powered phone systems can answer calls and schedule appointments. These systems reduce the workload on staff.
AI can handle patient questions, referrals, and basic triage through conversation-like interfaces. Automated answering works all day and night. This means patients reach help more easily, fewer calls are missed, and satisfaction improves. Staff then have more time to do harder tasks that need human judgment.
Automation also speeds up check-ins, insurance checks, and billing questions. This cuts wait times and lowers errors from manual data entry. IT managers must carefully connect AI tools with existing health record systems while following privacy rules. They must also keep human oversight ready for sensitive or urgent calls.
In busy outpatient clinics or primary care, AI front-office tools can make operations smoother. They help reduce no-shows and use resources better. Medical administrators should check if the cost of using AI is lower than running things without it by looking at admin cost savings and patient service results.
Compared to the EU’s clear AI Act, the U.S. has many different AI rules. The FDA’s 2025 guidance moves toward risk-based management, but no single federal law controls AI in healthcare broadly. This may cause different AI standards in different places, making it hard for providers to know if they follow the rules or are liable.
The U.S. rules mostly use existing healthcare laws, HIPAA for data, and FDA oversight for medical software. There is no unique AI law yet. Healthcare leaders need to watch that AI vendors follow FDA rules for software as a medical device (SaMD) and keep up with new legal cases about AI harm.
Because of these challenges, many U.S. groups look at global standards, including the EU AI Act, to guide AI governance. Joining groups that include many stakeholders or certification programs for trustworthy AI can help programs be safer.
Legal experts also suggest making internal policies. These policies should say what AI is allowed to do, how humans oversee AI, require training, and set up incident reporting that meets regulations.
By focusing on safety, liability, oversight, and openness when using AI, medical administrators, owners, and IT managers in the U.S. can add AI to healthcare work responsibly. Using lessons from worldwide regulations and adjusting them for U.S. conditions helps build AI practices that center on patient care, clinical effectiveness, and proper use of data while supporting more technology use in medicine.
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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