Medical device recalls and Anthropic’s proposed AI safety legislation highlight growing concerns over testing, independent oversight and government authority to prevent dangerous technology failures.
Artificial intelligence is creating new challenges for regulators as increasingly sophisticated systems enter hospitals, medical devices and other critical industries. On October 20, 2026, new discussions about medical device safety and a proposed federal AI law highlighted a central question: how can regulators protect the public when technology evolves faster than traditional testing and oversight?
The concerns span two distinct areas. Medical device manufacturers must monitor products that directly affect patient care, while developers of advanced AI models face questions about systems capable of contributing to cyberattacks, biological threats or other serious harm.
Both debates center on the need for effective testing, independent evaluation and continued monitoring after technology reaches the public.
Medical Device Recalls Raise Safety Concerns
The United States recorded more than 1,100 medical device recalls in 2025, including 145 Class I recalls, according to figures discussed in an October 20 interview published by Medical Design & Development Industry (MD+DI).
Class I is the Food and Drug Administration’s most serious recall classification. It applies when there is a reasonable probability that using or being exposed to a product will cause serious health consequences or death.
Rita King, founder and CEO of medical device consulting company MethodSense, said the figures do not necessarily mean the regulatory system has failed. Instead, she argued that manufacturers must recognize how increasingly complex technologies create new safety challenges.
King emphasized that companies remain responsible for monitoring medical devices after they reach the market. This includes reviewing complaints, identifying possible component failures and investigating patterns that could indicate emerging problems.
FDA authorization does not end a manufacturer’s responsibility to evaluate product safety. Continued monitoring is especially important when devices depend on software that may behave differently under real-world conditions.
The recall figures cover medical devices broadly and should not be interpreted as 1,100 recalls caused by artificial intelligence. They provide context for the wider safety system as AI becomes more common in medical technology.
AI Medical Devices Require Different Testing Approaches
AI-enabled medical devices are already used in several areas of health care, including medical imaging, disease detection and treatment support.
According to the FDA, more than 1,600 AI-enabled medical devices had received authorization for marketing in the United States as of September 2026. Examples include systems that analyze medical images, identify diabetic retinopathy and support insulin dosing.
Unlike conventional software, certain AI systems rely on patterns learned from large datasets. Their performance can depend on whether the patients, clinical environments and equipment encountered after deployment resemble those used during development.
A model tested with data from one population, for example, might perform differently when used with patients from another population. Manufacturers therefore need evidence that the technology works reliably across its intended uses.
King identified a practical challenge for developers: obtaining enough testing data without making clinical studies prohibitively expensive.
More extensive studies may improve confidence in a product’s safety and effectiveness, but they also increase development costs. Manufacturers must balance those demands while satisfying regulatory requirements.
Research published in JAMA Network Open in June 2026 examined clinical evidence and FDA recalls involving AI-enabled medical devices. The study adds to growing scrutiny of how such products are evaluated and monitored.
Software Regulation Offers Lessons for AI
King compared the industry’s current challenges with the introduction of software into medical devices during the 1990s.
At that time, manufacturers were still developing reliable methods for identifying software failures and documenting testing procedures. Over time, more formal software development and risk management practices became standard.
One important regulation was 21 CFR Part 11, introduced in 1997. It establishes requirements for certain electronic records and electronic signatures used in FDA-regulated activities.
Although Part 11 contains provisions related to computerized systems, it is not a comprehensive standard for validating medical device software. Other FDA requirements and guidance address device design, software verification and risk management.
King argued that AI presents another technological transition requiring manufacturers to reconsider established testing methods.
This becomes particularly important for autonomous or agentic AI systems, which can take actions to accomplish goals rather than simply produce information for a human user.
Such systems could create additional questions about human oversight, predictable behavior and whether existing safety evaluations remain adequate.
Anthropic Proposes Federal Oversight of Powerful AI Models
The debate over AI safety extends beyond medical devices. Anthropic, the company behind the Claude AI models, has circulated draft federal legislation intended to establish oversight of the most powerful AI systems.
Reuters reported on October 16 that the company had presented the proposal to lawmakers. TechCrunch published further details on October 20, including the circumstances under which an AI model’s release could be blocked.
The draft focuses on catastrophic risks rather than routine software errors or misleading responses.
Under the reported proposal, intervention could be considered when an advanced AI model presents a foreseeable and imminent risk of causing more than 50 deaths or serious injuries, or more than $1 billion in property damage.
The covered dangers include biological weapons, major cyberattacks and AI systems escaping their developers’ controls.
These thresholds are part of a proposed legal framework, not an existing nationwide standard for blocking AI releases.
Anthropic has confirmed that the draft reflects proposals contained in its Advanced AI Framework, which the company began promoting in June 2026.
Independent Evaluators Would Play a Central Role
Anthropic’s draft proposes using independent evaluators registered with the U.S. Commerce Department to assess potentially dangerous AI models.
These evaluators could be paid by the companies whose systems they examine, provided compensation does not depend on the evaluation’s findings.
That arrangement raises questions about financial independence. Critics of industry-funded evaluations argue that payment relationships can create conflicts of interest even when compensation is not directly tied to a particular result.
Supporters of specialized outside evaluations argue that independent testing can provide technical expertise that government agencies may not have internally.
The proposal would require evaluators who identify an imminent catastrophic risk to notify the Commerce Department within 72 hours.
The department would then seek a court order to prevent a dangerous model from being released. Under the reported draft, federal agencies would not independently possess authority to block distribution without judicial involvement.
The arrangement is intended to provide oversight while limiting unilateral government action. Whether it would allow officials to respond quickly enough during an emergency is one of the questions lawmakers would need to consider.
Federal and State AI Regulations Could Overlap
Anthropic’s proposal arrives as lawmakers consider competing approaches to AI oversight.
California has enacted legislation addressing catastrophic risks from powerful AI systems. Its approach includes transparency requirements and standards related to severe potential harm.
Anthropic’s broader framework supports federal rules for advanced AI developers while arguing that state protections should not be displaced unless federal safeguards are sufficiently strong.
The company has proposed mandatory testing, public safety disclosures, independent evaluations and government authority to act against dangerous deployments.
Other technology leaders have expressed different views about new regulation. Nvidia CEO Jensen Huang has argued that the industry does not need additional AI-specific laws, reflecting a debate over whether existing legal protections are sufficient.
Federal lawmakers also face questions about which agency should oversee advanced systems and how responsibilities should be divided between national and state authorities.
Regulation Must Account for Different Types of AI Risk
Medical device regulation and frontier AI legislation address different technological problems.
Medical devices generally undergo product-specific reviews involving defined medical purposes, patient populations and evidence of safety and effectiveness. Manufacturers also face continuing obligations after their products enter the market.
Frontier AI systems can be used for many purposes, making their possible effects harder to evaluate through a single application-specific review.
The proposed federal legislation concentrates on rare but potentially catastrophic outcomes. It does not replace the separate regulatory requirements governing medical devices or other specialized products.
Experts also distinguish between systems that merely generate recommendations and systems capable of independently performing actions. Greater autonomy may require additional safeguards, monitoring and methods for interrupting unsafe behavior.
For medical manufacturers, the immediate priority remains ensuring that products perform safely throughout their intended lives. For developers of powerful general-purpose AI, regulators are considering how to identify dangerous capabilities before widespread deployment.
Regulators Face Decisions About Testing and Accountability
The FDA already maintains recall databases, adverse-event reporting systems and procedures for communicating serious medical device risks. Those mechanisms provide ways to identify problems after products begin reaching patients.
Anthropic’s proposed legislation would establish a different system, focused on independent assessment and possible court intervention before certain advanced AI systems are deployed.
Neither approach eliminates the possibility of unexpected failures. Their effectiveness depends on reliable testing, accurate reporting and the authority to respond when evidence reveals unacceptable risks.
The medical device industry must continue adapting established safety practices as AI becomes more capable. Congress, meanwhile, has not enacted Anthropic’s proposed framework and would need to resolve questions about oversight authority, evaluator independence and federal enforcement before such a system could become law.
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