National Health Council (Randall L. Rutta)

See attached file(s)

October 19, 2026

Dockets Management Staff (HFA-305)
U.S. Food and Drug Administration
5630 Fishers Lane, Rm. 1061
Rockville, MD 20852

RE: Considerations for the Regulation of Generative AI-Enabled Medical Devices:
Discussion Paper and Request for Feedback [FDA-2026-N-7874]

Submitted electronically via regulations.gov

Dear Acting Commissioner Diamantas:

The National Health Council (NHC) appreciates the opportunity to provide comments on
the Food and Drug Administration’s (FDA’s) discussion paper, Considerations for the
Regulation of Generative AI-Enabled Medical Devices. The discussion paper addresses
a rapidly evolving area of medical product development and appropriately recognizes
both the potential for GenAI-enabled devices to improve care and the need to address
risks associated with open-ended outputs, evolving models, third-party foundation
models, and increasing levels of autonomy. The NHC supports FDA’s effort to seek
early stakeholder input and to consider a risk-based, total product lifecycle approach
that can support timely access to safe and effective devices.1

The NHC unites nearly 200 national organizations—including leading patient groups,
research institutions, providers, caregivers, and businesses across the health care
sector—to drive patient-centered health policy. Representing 200+ million Americans
with chronic diseases and disabilities, the NHC strengthens its members’ collective
influence to expand access to quality, affordable, and equitable health care. The NHC
fosters collaboration to shape policies that reflect the needs of patients.

Across its work on artificial intelligence (AI) in health care, the NHC has emphasized
that innovation should improve patient care while maintaining safety, effectiveness,
transparency, accountability, privacy, and meaningful human involvement. In testimony
to Congress, the NHC emphasized that AI should support and supplement—not
supplant—human decision-making and patient preferences. The testimony also
highlighted the importance of pre-deployment testing, meaningful oversight,
understandable information, and continuous feedback are to responsible AI use. The
NHC’s Principles on Health AI identify patient health benefit, patient-centeredness,

1 U.S. Food and Drug Administration, Considerations for the Regulation of Generative AI-Enabled Medical

Devices: Discussion Paper and Request for Feedback (August 2026), 1–3,
https://www.fda.gov/media/189158/download.
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education and empowerment, equity and accessibility, trustworthiness, comprehensive
transparency, bias mitigation, privacy and security, human collaboration, continuous
improvement, safety and effectiveness, accountability, and ethical considerations as
foundational expectations for health AI.2,3

These principles are particularly relevant to GenAI-enabled medical devices. Many of
the characteristics that may make GenAI useful—including flexibility, natural-language
interaction, personalization, and multimodality—can also complicate efforts to evaluate
performance and identify failures. The NHC supports a regulatory framework that is riskproportionate, scientifically rigorous, predictable, and sufficiently flexible to
accommodate technological change. As FDA develops that framework, the NHC
encourages the Agency to incorporate patient experience and patient-facing
consequences throughout risk assessment, premarket evaluation, change control, and
postmarket monitoring.

Summary of Recommendations

• Use the proposed two-axis risk framework as a starting point while accounting for
additional considerations, including reversibility, detectability of error, time
sensitivity, traceability to source information, reliance and automation bias, user
context, and the availability of meaningful human review.

• Evaluate patient-facing functions based on clinical context and the consequences
of reliance rather than presuming higher risk solely because the intended user is
not a health care professional. Relevant considerations include the directiveness
and personalization of the output, the user’s practical ability to recognize
uncertainty or error, and the availability of appropriate safeguards.

• Support a competency-based premarket framework that combines riskproportionate benchmarking with clinical confirmation and aligns the amount and
type of evidence with intended use, real-world conditions, and the consequences
of error.

• Encourage evaluation across clinically relevant populations and deployment
contexts, including differences in health literacy, language, disability, age,
geography, and other factors that can affect how patients interact with and
interpret GenAI outputs. Synthetic data can be a useful supplement but should
not routinely replace evidence from real patients and real-world settings.

• Incorporate patient input into communication, comprehension, usability, and
escalation benchmarks, as well as judgments about meaningful performance and

2 National Health Council, Principles on Health AI, March 25, 2025,

https://acrobat.adobe.com/id/urn:aaid:sc:va6c2:1e4c2cb5-5b01-4e65-b386-8eeefea9b6f5.

3 National Health Council, “NHC Statement on Artificial Intelligence and Health Care: Promise and

Pitfalls,” statement for the record submitted to the Senate Committee on Finance, February 8, 2024,
https://nationalhealthcouncil.org/letters-comments/nhc-statement-on-artificial-intelligence-and-healthcare-promise-and-pitfalls/.
NHC Feedback on Regulatory Approach for Generative AI-Enabled Medical Devices
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acceptable uncertainty. This input should help ensure that regulatory evaluation
reflects how patients and caregivers understand and act on GenAI-generated
information.

• Use postmarket monitoring as a central component of total product lifecycle
oversight . Allow greater reliance on postmarket evidence to address premarket
uncertainty only when the device’s risk, reversibility, monitoring capability, and
available intervention pathways make that approach appropriate.

• Encourage transparent, risk-based postmarket monitoring that can detect
performance drift, subgroup disparities, recurrent failure modes, and changes in
patient-facing behavior, with clear triggers for reassessment and mechanisms for
patients and clinicians to report concerns.

• Maintain clear manufacturer accountability when responsibility is shared across
foundation-model developers, health systems, clinicians, standards
organizations, and other ecosystem participants. Contracts, audit logs, update
notifications, version controls, and predetermined change control plans can help
manage these dependencies while preserving clear responsibility for the
regulated device.

• Explore voluntary Foundation Model Device Master Files as a potentially useful
mechanism to improve regulatory efficiency and consistency, particularly when
they provide standardized, current, and versioned information on model
characteristics, limitations, safety controls, update practices, and subgroup
performance. The device sponsor should retain responsibility for safety and
effectiveness of the final deployed device.

• Use additional, risk-proportionate safeguards for agentic GenAI-enabled devices
as autonomy and the consequences of action increase, including clearly defined
permissions, meaningful human checkpoints before irreversible or highconsequence actions, auditable action logs, fail-safe behavior, rollback
mechanisms, and evaluation of cumulative multi-step failure and cybersecurity
risks.

• Maintain a technology- and architecture-neutral framework while allowing testing
expectations to reflect architecture-specific failure modes for multimodal, visionlanguage, predictive, and other emerging models when those differences may
affect safety, effectiveness, or patient understanding.

Risk Assessment Should Reflect Patient Context and Real-World Consequences

The NHC supports FDA’s proposal to assess risk based on the core dimensions of
device activity and the consequences of relying on an incorrect output. These
dimensions appropriately distinguish among tools that provide information, tools that
direct users toward a particular action, and tools that take action with limited or no
human intervention. They also recognize that similar levels of autonomy may carry
different risks depending on the clinical consequences of error. The NHC encourages
NHC Feedback on Regulatory Approach for Generative AI-Enabled Medical Devices
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FDA to organize risk assessment around these dimensions while allowing additional
factors to modify the resulting risk determination.

Additional considerations may include reversibility, detectability of error, time sensitivity,
traceability, and the availability of downstream safeguards. An incorrect output that is
readily recognizable, easily corrected, and unlikely to cause harm before a clinician can
intervene presents a different risk profile from an output that appears authoritative,
cannot be independently checked, and may prompt an irreversible action under time
pressure. Traceability is particularly relevant for GenAI because a fluent response may
convey more certainty than the underlying evidence supports. FDA’s framework would
benefit from considering whether the intended user can understand the basis and
limitations of the output, recognize uncertainty, and obtain meaningful review before
acting.

This approach is consistent with the NHC’s earlier comments on FDA’s AI-enabled
device software function guidance, where we encouraged rigorous validation, clear
disclosure of intended use and limitations, subgroup-specific performance assessment,
and ongoing verification throughout the total product lifecycle. It also reflects concerns
raised by the patient and medical technology communities regarding algorithmic bias,
clinical transparency, distribution shift, cybersecurity, privacy, and the importance of
appropriate human review.4,5,6

Patient-Facing Functions Warrant Context-Specific, Patient-Centered Evaluation

The discussion paper appropriately considers whether patient-facing informational
functions may present different risks from functions intended for clinicians. It also
recognizes the potential benefits of improved access to information, patient
empowerment, and engagement. The NHC appreciates this balanced framing. Patients
vary widely in health literacy, disease expertise, lived experience, access to clinicians,
and familiarity with digital tools, and many people living with chronic or rare disease
develop substantial expertise in their own conditions. FDA’s framework should therefore
avoid treating patient-facing use as categorically higher risk based solely on the
intended user’s professional credentials.

At the same time, GenAI systems can generate highly personalized and persuasive
language that patients may reasonably interpret as tailored medical advice. The NHC
encourages FDA to evaluate how strongly an output directs a user toward a particular
action, considering specificity, personalization, confidence, context, repetition across a
conversation, and whether the model adapts its recommendation after receiving

4 Alzheimer’s Association, “Technology Safety for Older Adults,” accessed October 6, 2026,

https://www.alz.org/help-support/caregiving/safety/technology-safety-older-adults.

5 Advanced Medical Technology Association (AdvaMed), The Role of Artificial Intelligence (AI) in

Healthcare, November 11, 2024, https://www.advamed.org/member-center/resource-library/the-role-ofartificial-intelligence-ai-in-healthcare/.
6 National Health Council, “NHC Submits Comments on FDA Draft Guidance for AI/ML-Enabled Medical

Devices,” April 7, 2025, https://nationalhealthcouncil.org/letters-comments/nhc-submits-comments-on-fdadraft-guidance-for-ai-ml-enabled-medical-devices/.
NHC Feedback on Regulatory Approach for Generative AI-Enabled Medical Devices
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additional patient information. A generic explanation of treatment options, for example,
presents different considerations from a response that integrates an individual’s
symptoms and medications and recommends a specific course of action.

The relevant regulatory question is therefore whether the intended user has a realistic
opportunity to understand the output’s limitations, recognize uncertainty or error, and
obtain appropriate review before consequential action occurs. Depending on intended
use and risk, safeguards may include plain-language explanations of uncertainty,
source traceability, explicit boundaries on intended use, escalation pathways,
confirmation steps, and human review for higher-consequence recommendations.
Consistent with the NHC’s Principles on Health AI , these safeguards can protect
patients while preserving the benefits of access to useful information.7

FDA would also benefit from distinguishing disclosure from consent and tailoring
patient-facing information to the role the system plays in care. Not every use of a
GenAI-enabled device will warrant the same type or depth of explanation. When a
function materially shapes a diagnosis, treatment recommendation, triage decision, or
other consequential interaction, however, patients should receive understandable
information about what the tool is intended to do. That information should explain
whether its output is advisory or action-directing, the role of any human reviewer,
important limitations, and how to seek review if the output appears inconsistent with
their experience. The NHC encourages FDA to consider comprehension and usability,
rather than the presence of disclosure alone, when evaluating patient-facing
communication.

These considerations also apply when a caregiver is the primary or frequent user of a
device. In pediatric care, dementia, disability, serious illness, and other settings, family
caregivers may help interpret information and support decision-making. Evaluation
should reflect these real-world patterns of use, including whether information is
understandable to the person interacting with the device, whether escalation
instructions are actionable, and whether the system appropriately distinguishes the
roles of patients and caregivers. A patient-centered approach can account for caregiver
involvement without treating it as a substitute for patient autonomy where the patient
can participate directly.

A similar approach is appropriate when considering differences between generalist and
specialist clinicians. The NHC does not believe specialty status alone should determine
risk. Risk may increase when safe use depends on knowledge the intended user cannot
reasonably be expected to possess, when an output falls outside the user’s usual scope
of practice, or when specialist-level conclusions are presented without sufficient
information about uncertainty, limitations, or the need for consultation. FDA should
consider the knowledge reasonably available to the intended user, the clinical
consequences of error, and whether the device provides adequate context and
pathways for referral or consultation. A GenAI-enabled function that responsibly extends

7 NHC, Principles on Health AI. See Principle 2: Patient-Centered and Informed; Principle 3: Patient

Education and Empowerment; Principle 5: Trustworthiness; Principle 6: Comprehensive Transparency;
and Principle 9: Human Collaboration.
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access to specialty knowledge may meaningfully benefit patients, particularly in settings
where specialist access is limited.8

Multi-Turn Conversations and Care Escalation Should Be Evaluated Under
Realistic Conditions

The NHC agrees that GenAI risk cannot always be assessed one interaction at a time.
A conversational device may move from general education to personalized
interpretation and then to action-directing guidance over the course of an exchange.
Evaluation would therefore benefit from testing realistic conversations that include long
interactions, contradictory information, changing symptoms, emotional distress,
repeated requests for out-of-scope recommendations, and circumstances in which a
user minimizes or exaggerates symptoms. The conversation as a whole should remain
consistent with the device’s intended use and safety parameters.

For care escalation functions, both under-escalation and over-escalation can harm
patients. Failure to identify an emergency can delay necessary treatment, while
systematic over-escalation can create anxiety, unnecessary testing, avoidable
emergency department use, financial burden, and reduced trust. The NHC encourages
FDA to allow these errors to be weighed differently depending on clinical context rather
than relying on a single undifferentiated measure of performance.

Evaluation of escalation would also benefit from clinically interpretable information
beyond a single sensitivity or specificity estimate. Sponsors could characterize the types
of cases in which under- and over-escalation occur, the severity and reversibility of
resulting harms, and whether errors are concentrated in particular populations,
symptom presentations, or conversational patterns. Aggregate performance can
obscure clinically important differences. FDA should therefore encourage reporting that
helps reviewers understand relevant tradeoffs and identify where additional safeguards
or evidence may be needed.

A Competency-Based Premarket Framework Can Support Flexible, Fit-forPurpose Evaluation

The NHC supports FDA’s exploration of a competency-based approach that combines
device benchmarking with clinical confirmation. Exhaustive testing of every possible
input and output is unlikely to be practical for open-ended GenAI systems. A structured
set of competencies may provide a scalable way to evaluate safety behavior, clinical
proficiency, communication, robustness, subgroup performance, and agentic
capabilities. The approach is most useful when it applies to the final deployed
configuration and when the rigor of evaluation is proportionate to intended use and risk.

Benchmarking alone, however, may not fully demonstrate how a device will perform in
the settings where patients and clinicians use it. Public benchmarks may be affected by

8 American Diabetes Association, “New ADA Scientific Statement: Diabetes Technology in Primary Care,”

September 28, 2026, https://diabetes.org/newsroom/press-releases/new-ada-scientific-statementdiabetes-technology-primary-care.
NHC Feedback on Regulatory Approach for Generative AI-Enabled Medical Devices
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contamination or saturation, and benchmark datasets may not reflect the workflows,
language patterns, comorbidities, or uncommon but consequential scenarios
encountered in practice. The NHC therefore supports FDA’s inclusion of clinical
confirmation and encourages the Agency to treat benchmarking and clinical
confirmation as complementary sources of evidence. Benchmarking can efficiently test
broad capabilities and known failure modes, while clinically representative evidence can
help establish whether those capabilities translate into safe and effective real-world
performance.

The NHC also encourages FDA clarify the characteristics of fit-for-purpose benchmarks
used to support regulatory decisions. Relevant considerations include whether a
benchmark measures a capability meaningfully connected to the intended clinical use;
reflects the difficulty, input distribution, and failure modes expected in practice; uses
prespecified scoring and acceptance criteria; and produces reproducible results.
Sequestered or independently maintained test sets may be useful where they reduce
contamination or overfitting. Sponsor-developed benchmarks may also be appropriate
when no suitable external benchmark exists, provided that sponsors explain the
benchmarks’ development methods, data provenance, representativeness, and
relationship to clinically relevant performance.

This position builds on the NHC’s comments responding to an NIH RFI, in which the
NHC supported external validation on diverse, representative datasets; standardized
performance benchmarks; simulation environments for edge cases; third-party auditing
tools; and independent evidence of safety, effectiveness, and equity before clinical
deployment of federally supported AI tools. It is also consistent with the NHC’s broader
position that rigor should be fit for purpose rather than equated with a single evidentiary
method. The amount and type of evidence should be driven by the consequences of
error, the degree of autonomy, the novelty of the use case, the feasibility of human
review, and the degree to which performance can be reliably observed and corrected
after deployment.9,10

FDA would also benefit from explaining how the competency-based framework aligns
with different levels of clinical confirmation. A lower-risk informational function with
transparent sources, easily reversible outputs, and reliable human review may be
adequately supported by rigorous benchmarking together with retrospective or shadowdeployment evidence. A patient-facing function that provides individualized actiondirecting guidance may warrant standardized-patient testing, prospective observation,
or other evidence demonstrating that intended users understand and respond
appropriately to the output. For autonomous or high-consequence functions,
prospective clinical evidence may be appropriate when residual uncertainty cannot
reasonably be resolved through benchmarking, simulation, or retrospective data alone.

9 National Health Council, “NHC Responds to NIH Artificial Intelligence Strategy RFI,” July 15, 2025,

https://nationalhealthcouncil.org/letters-comments/nhc-responds-to-nih-artificial-intelligence-strategy-rfi/.

10 Biotechnology Innovation Organization (BIO), “BIO Comments to FDA on Using Artificial Intelligence

and Machine Learning in the Development of Drug and Biological Products,” August 3, 2023,
https://www.bio.org/letters-testimony-comments/bio-comments-fda-using-artificial-intelligence-andmachine-learning.
NHC Feedback on Regulatory Approach for Generative AI-Enabled Medical Devices
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The central question should be whether the evidence package is sufficient for the
intended use and clinical context rather than whether one study design is preferred in all
cases.

Clinical confirmation should also consider the interaction between the device and the
surrounding workflow. Performance may differ when clinicians are operating under time
pressure, when staffing is limited, when a system is integrated into an electronic health
record, or when a patient receives an output directly outside a clinical encounter.
Automation bias, alert fatigue, confirmation bias, and overreliance on polished language
can affect how otherwise accurate information is used. The NHC encourages FDA to
consider these human and organizational conditions when they are material to the
device’s benefit-risk profile.

Benchmarks Should Reflect the Intended Population, Communication Needs, and
Real-World Alternative

The proposed benchmarking elements provide a strong starting point, particularly
safety-critical recognition and escalation, boundary adherence, calibration and deferral,
clinical analysis, communication quality, robustness, subgroup performance, and
agentic competencies. The NHC encourages FDA to treat patient comprehension and
usability as substantive performance considerations rather than secondary interface
issues. A clinically accurate response may still provide limited value or create risk if
patients misunderstand it, cannot distinguish uncertainty from certainty, or are unable to
identify the action the device is asking them to take.11

Evaluation should therefore include users with varied health literacy, digital literacy,
language proficiency, disability status, age, geography, and familiarity with the relevant
disease area. It should also test nonstandard phrasing, colloquialisms, incomplete
histories, caregiver-mediated interactions, and accessibility features where relevant.
Patient organizations can help identify the terminology, scenarios, burdens, and failure
modes that matter in particular disease communities. The NHC’s Patient Experience
Mapping Toolbox illustrates how structured patient input can identify the real-world
steps, barriers, and impacts that are often invisible in conventional development
processes. This input can also inform fit-for-purpose evaluation design.12

Patient organizations do more than recruit participants for usability testing. They can
help identify the situations in which errors would be most consequential to a particular
disease community, the language patients use to describe symptoms and treatment
experiences, and the practical barriers that affect whether an escalation
recommendation can be followed. For chronic and rare diseases in particular, patients
may encounter nuanced treatment tradeoffs, fragmented specialty care, or atypical
manifestations that are poorly represented in general-purpose datasets. The NHC

11 Medical Device Innovation Consortium, “Patient Centered Digital Health,” accessed October 6, 2026,

https://mdic.org/our-work/patient-centered-digital-health/.
12 Elisabeth M. Oehrlein et al., on behalf of the Patient Experience Mapping Working Group, Patient

Experience Mapping Toolbox (Washington, DC: National Health Council, 2021),
https://nationalhealthcouncil.org/additional-resources/patient-experience-map/.
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encourages FDA to support early, fit-for-purpose engagement with patient and caregiver
communities when benchmark scenarios, endpoints, communication criteria, and
postmarket monitoring priorities are developed.13,14

Comparator selection should likewise reflect the clinical question and the actual
alternative available in practice. In some settings, specialist consensus may be
appropriate; in others, the relevant alternative may be generalist care, delayed specialist
review, no intervention, or a clinician using the device as part of a human-AI team.
Requiring every GenAI system to outperform a specialist acting alone could be
unnecessarily restrictive when the intended benefit is improved access in settings where
specialist care is not readily available. Conversely, comparison with a median clinician
may not be sufficient for a highly autonomous system making decisions with irreversible
or life-threatening consequences. FDA should preserve flexibility while asking sponsors
to justify why the selected comparator reflects the intended use and decision
environment.

When a device is designed to augment rather than replace a clinician, FDA should also
consider the performance of the human-AI team. A system that performs well in
isolation may add limited value if its outputs are difficult to interpret, interrupt workflow,
or encourage inappropriate reliance. Conversely, a system with imperfect standalone
performance may improve care if it reliably helps clinicians recognize relevant
alternatives or identify cases that might otherwise be missed. Evaluation can therefore
consider whether the device changes clinician behavior, whether users appropriately
accept and reject recommendations, and whether the combined system performs safely
across realistic workloads and settings.

Synthetic Data Can Supplement Real-World Evidence When Fit for Purpose

The NHC recognizes the potential value of synthetic data for rare scenarios,
underrepresented subgroups, stress testing, and simulation of clinically important edge
cases. Synthetic inputs may be particularly useful when real patient data are sparse or
prospective patient exposure would be inappropriate. At the same time, synthetic data
may reproduce assumptions and limitations in the models or datasets used to generate
them. FDA should therefore consider the role of synthetic data in relation to the specific
evidentiary question and the availability of complementary real-world information.

The NHC encourages FDA to ask sponsors using synthetic data to describe how those
data were generated, demonstrate their relevance to the intended population or
deployment environment, and explain which performance questions the data can and
cannot address. Synthetic evidence may be particularly useful as a supplement for
stress testing and rare scenarios but should not routinely displace real patient data for
evaluating subgroup performance, communication, usability, or other clinically

13 Medical Device Innovation Consortium, “Elevating Your RWE Strategy With Direct Patient Input: A

Framework for Direct Patient Input in Real World Data and Evidence Generation,” accessed October 6,
2026, https://mdic.org/resources/framework-direct-patient-input-rwe/.

14 NHC, Principles on Health AI. See Principle 2: Patient-Centered and Informed; Principle 3: Patient

Education and Empowerment; and Principle 13: Ethical Considerations.
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consequential behavior. Where important subgroups are difficult to characterize
premarket, a focused postmarket evidence plan may help address residual uncertainty.

The NHC also supports a fit-for-purpose statistical approach rather than a single
formula for GenAI-enabled devices. Sample size, precision, and uncertainty should
reflect intended use, the consequences of error, the expected frequency of safetycritical scenarios, and clinically relevant subgroup analyses. Sponsors should prespecify
endpoints and analysis plans and provide sufficient evidence to characterize
performance where failures could materially affect patients.

For open-ended outputs, performance assessment may need to combine several
measures rather than rely on a single accuracy endpoint. Depending on intended use,
relevant measures could include safety-critical failure rates, calibration, consistency
across repeated runs, agreement with adjudicated clinical judgments, intended-user
comprehension, time to appropriate escalation, and subgroup-specific performance.
The NHC encourages FDA to promote uncertainty reporting that informs regulatory
decisions. A large volume of routine interactions should not obscure rare but severe
failure modes. Sponsors should also explain how ambiguous or partially correct outputs
are scored and how disagreement among human adjudicators is handled.

Similarly, benchmarking and clinical-confirmation evidence need not be combined into a
single estimate merely because both can be expressed numerically. Integration may be
appropriate when datasets, endpoints, distributions, reference standards, and testing
conditions are sufficiently comparable, and the analytical approach is prospectively
justified. Where synthetic cases, retrospective records, standardized patients, and
prospective encounters represent meaningfully different environments, separate
reporting may provide a clearer picture of performance and uncertainty before FDA
considers any combined estimate.

Independent Third Parties Can Support Evaluation While Preserving Flexibility

Qualified independent third parties may add value by maintaining sequestered
benchmarks, conducting specialized testing, providing expert adjudication, or
developing FDA-qualified tools. Independent infrastructure can reduce the risk of
optimizing narrowly for benchmark performance and avoid unnecessary duplication
across sponsors. The NHC encourages FDA to preserve flexibility in how sponsors
demonstrate scientific rigor so that third-party participation does not become a de facto
requirement, create avoidable development burden, or disadvantage smaller innovators.
Qualifications, conflicts of interest, methods, and data-governance practices should be
transparent.

The Framework Should Remain Technology-Neutral While Accounting for
Architecture-Specific Risks

The NHC encourages FDA to preserve a technology- and architecture-neutral
regulatory framework focused on intended use, patient risk, and demonstrated
performance rather than creating separate regulatory categories for each emerging
model architecture. At the same time, evidentiary expectations should reflect
architecture-specific failure modes. For multimodal vision-language systems, for
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example, evaluation may need to consider whether information from different modalities
is integrated consistently; whether missing, corrupted, or conflicting inputs are
recognized; whether errors in one modality affect downstream reasoning; and whether
the system can communicate which inputs materially influenced a conclusion. Other
emerging architectures may raise different questions about temporal consistency,
simulation fidelity, uncertainty, or the consequences of predictions that are later used to
direct or take action.

Multimodal systems illustrate why this flexibility is important. A device may perform well
when text, images, laboratory values, and other inputs are evaluated separately but
behave differently when those inputs conflict, arrive at different times, or are incomplete.
The NHC encourages FDA to allow testing methods to reflect these foreseeable
conditions and to focus on whether the complete device performs reliably in its intended
environment. For generative or predictive world models, the relevant question is
similarly whether model outputs translate into reliable clinical behavior rather than
whether the underlying simulation appears realistic.

A technology-neutral approach can also make the framework more durable as new
model types emerge. Rather than attempting to anticipate every architecture, FDA can
identify the safety properties, evidence expectations, and lifecycle controls that should
be demonstrated for the intended use and allow the specific testing methods to evolve
with the technology.

Postmarket Monitoring Is Essential to a Total Product Lifecycle Approach

The NHC strongly supports a total product lifecycle approach for GenAI-enabled
devices. Variable outputs, changing deployment environments, model updates, and
dependencies on third-party foundation models make ongoing evaluation particularly
important. The NHC has previously encouraged robust postmarket surveillance and
continuous verification of AI-enabled devices, including audit trails, monitoring of model
updates, and mechanisms to identify changes in subgroup performance and real-world
reliability. This approach is also consistent with stakeholder work emphasizing ongoing
quality systems, postmarket surveillance, user feedback, and adverse event monitoring
as components of FDA’s lifecycle framework.15,16

FDA should exercise caution when deciding whether to allow greater premarket
uncertainty in exchange for stronger postmarket monitoring. Such an approach may be
appropriate for lower- or moderate-risk functions where errors are readily detectable
and reversible, monitoring can identify deterioration promptly, and there is a reliable
mechanism to intervene before harm accumulates. Greater reliance on postmarket
evidence may be less appropriate for highly autonomous functions, irreversible actions,

15 National Health Council, “NHC Submits Comments on FDA Draft Guidance for AI/ML-Enabled Medical

Devices,” April 7, 2025, https://nationalhealthcouncil.org/letters-comments/nhc-submits-comments-on-fdadraft-guidance-for-ai-ml-enabled-medical-devices/.
16 Advanced Medical Technology Association (AdvaMed), “AdvaMed Responds to Congressional RFI on

AI in Health Care,” May 9, 2024, https://www.advamed.org/industry-updates/news/advamed-responds-tocongressional-rfi-on-ai-in-health-care/.
NHC Feedback on Regulatory Approach for Generative AI-Enabled Medical Devices
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decisions with life-threatening consequences, or settings where failures are difficult to
detect or attribute. The NHC encourages FDA to make these distinctions explicit so that
postmarket monitoring complements, rather than substitutes for, evidence that can
reasonably be generated before deployment.

Postmarket plans should be specific enough to support meaningful oversight. Relevant
elements may include performance thresholds, subgroup analyses, sampling methods,
reassessment freqyebcy, triggering events, escalation procedures, and the actions
expected when thresholds are crossed. Triggering events may include clinically
meaningful changes to the foundation model, prompts, retrieval sources, guardrails,
orchestration logic, user interface, intended population, deployment setting, or
connected tools—not only formal retraining events.

A credible postmarket approach should also distinguish findings suited to routine review
from signals that warrant more immediate action. Some findings may appropriately be
evaluated at a regularly-scheduled review. Others—such as a material increase in a
safety-critical failure, a foundation-model update that changes refusal or escalation
behavior, or evidence of deteriorating performance in a clinically important subgroup—
may call for prompt investigation or mitigation. The NHC encourages FDA to promote
prospective planning for these response pathways to the extent practicable.

Postmarket monitoring can also support learning when important uncertainties remain at
authorization, but it should not become a default substitute for feasible premarket
evidence. Real-world evidence may be particularly valuable for identifying rare failure
modes, changes in population mix, and performance across varied workflows. At the
same time, postmarket data may be observational, incomplete, or difficult to interpret.
Where FDA accepts greater premarket uncertainty, the NHC encourages clear dataquality expectations, preservation of relevant logs and version information, and a
defined plan for how postmarket evidence will address the remaining uncertainty.17

Patient and Clinician Feedback Should Inform Postmarket Monitoring

Periodic re-benchmarking, clinician review, and drift monitoring are important, but they
may not capture every failure mode that emerges in practice. The NHC encourages
FDA to support practical mechanisms for patients, caregivers, and clinicians to report
confusing, unsafe, discriminatory, or repeatedly incorrect outputs, including near misses
that may not meet traditional adverse-event thresholds. Complaints, support channels,
user feedback, and patient-organization input can provide useful signals and help
identify patterns that structured testing may miss.

17 Medical Device Innovation Consortium, NEST Research Methods Framework: A Practical Guide to

Real-World Evidence (RWE) for Medical Devices, accessed October 6, 2026,
https://mdic.org/resources/nest-research-methods-framework-a-practical-guide-to-real-world-evidencerwe-for-medical-devices/.
NHC Feedback on Regulatory Approach for Generative AI-Enabled Medical Devices
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Where feasible, manufacturers should monitor whether performance changes across
relevant subgroups and interaction contexts over time.18 A system can remain stable on
an aggregate benchmark while becoming less reliable for a smaller population, when
responding to a particular language pattern, or in a care setting experiencing distribution
shift. These concerns are consistent with broader clinical and stakeholder work
identifying dataset limitations, embedded disparities, accountability, reliability,
cybersecurity, and the ethics of patient data as important considerations for AI in health
care.19

Subgroup monitoring should be clinically motivated and sufficiently granular to identify
meaningful disparities while recognizing the limitations of small datasets. Relevant
subgroups will vary by intended use and may be defined by age, disability, language,
sex, race and ethnicity, geography, disease severity, comorbidities, socioeconomic
barriers, or other factors that affect available inputs or interpretation of outputs. FDA
should encourage sponsors to explain why selected subgroup analyses are relevant
and how sparse data will be addressed. When premarket evidence cannot reliably
characterize a small population, that limitation should be clearly disclosed and, where
appropriate, paired with a focused postmarket plan.

Equity considerations should include access and usability as well as algorithmic
performance. A device may perform similarly across demographic groups but still have
unequal effects if it assumes reliable broadband, high digital literacy, English
proficiency, ready access to follow-up care, or the ability to pay for recommended
services. For patient-facing GenAI devices, FDA should consider whether foreseeable
access barriers affect the consequences of an output and whether communication and
escalation pathways are workable for the populations the device is intended to serve.
These considerations are particularly important when a device is expected to expand
access in rural, underserved, or specialist-shortage settings.

Machine-Based Supervisory Agents May Support Monitoring but Require
Independent Evaluation

Machine-based supervisory agents may help scale monitoring by detecting anomalous
outputs, policy violations, drift, or changes in conversational behavior. Their use,
however, does not eliminate the need to evaluate the monitoring function itself.
Supervisory systems may share data sources, architecture, assumptions, or failure
modes with the device they monitor. The NHC encourages FDA to clarify expectations
for validating supervisory agents, characterizing false-negative and false-positive rates,
defining when human review is triggered, and periodically assessing whether the
monitoring approach continues to identify known and emerging failure modes.

18 NHC, Principles on Health AI. See Principle 4: Equity and Accessibility; Principle 7: Bias Mitigation;

Principle 10: Continuous Improvement; and Principle 11: Safety and Effectiveness.
19 American Heart Association, “Pragmatic Approaches to the Evaluation and Monitoring of Artificial

Intelligence in Health Care,” November 10, 2025, https://professional.heart.org/en/sciencenews/pragmatic-approaches-to-the-evaluation-and-monitoring-of-artificial-intelligence-in-healthcare.
NHC Feedback on Regulatory Approach for Generative AI-Enabled Medical Devices
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Shared Postmarket Roles Should Preserve Clear Manufacturer Accountability

The NHC agrees that clinicians, health systems, professional societies, standards
organizations, patient organizations, payers, and other stakeholders can contribute to
postmarket monitoring and responsible deployment. Health systems can identify local
workflow problems and distribution shifts; professional societies can help define
clinically appropriate performance expectations; patient organizations can identify
potential patient harm and communication concerns; and standards organizations can
support common testing methods. These complementary roles can strengthen oversight
while preserving the device manufacturer’s responsibility for the safety and
effectiveness of the marketed device.

Clear expectations are especially important when devices depend on third-party
foundation models. The NHC encourages FDA to consider how contractual and
technical mechanisms can support timely notice of material model changes, version
identification, access to safety-relevant documentation, and the ability to evaluate or
temporarily suspend affected functionality when necessary. Audit logs can also support
reconstruction of the model version, prompt configuration, retrieval source, and tool
environment associated with a consequential output. If a third-party dependency cannot
be adequately monitored or managed, that limitation should be considered in the
device’s benefit-risk assessment and change-control approach.

The level of visibility and control expected of the device manufacturer should be
proportionate to the role the foundation model plays in the regulated function. At a
minimum, manufacturers should be able to identify the model version in use, detect
material changes, understand known limitations relevant to intended use, and evaluate
whether an update changes safety-critical behavior. For higher-risk devices, advance
notice, change documentation, access to evaluation information, or continued access to
a previously validated model version may be important safeguards. FDA should
consider these dependencies when evaluating the adequacy of a sponsor’s changecontrol strategy.

Patients and clinicians should also understand who is responsible for addressing
problems. A complex chain of model developers, device manufacturers, health systems,
integrators, and clinicians can make it difficult for users to know where to report a
problem or who will investigate it. The NHC encourages FDA to promote clear labeling
and reporting pathways and to maintain a clear point of accountability for coordinating
investigation and corrective action, including when a third-party component contributes
to the problem.

Predetermined Change Control Plans Can Support Responsible Adaptation

The NHC supports the use of predetermined change control plans (PCCPs) where
future modifications can be described with enough specificity to define the methods,
acceptance criteria, and boundaries for making changes without undermining safety or
effectiveness. FDA’s existing PCCP guidance provides an important foundation for
adaptive AI-enabled device software functions. For GenAI systems, the challenge will
NHC Feedback on Regulatory Approach for Generative AI-Enabled Medical Devices
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be to define change categories by function so that they remain useful even when the
exact future implementation cannot be predicted.20,21

A GenAI PCCP could, for example, prespecify classes of foundation-model updates,
retrieval-source changes, prompt or guardrail modifications, or performance-tuning
changes, together with the re-benchmarking and clinical evidence appropriate for each
class. The NHC encourages FDA to provide sufficient guardrails around broadly defined
change categories so that flexibility does not reduce clarity regarding when additional
testing or Agency review is warranted. As future modifications become more difficult to
characterize in advance, clear post-change testing, version control, monitoring, and
notification thresholds become increasingly important.

The extent of reassessment should reflect the expected clinical impact of a modification.
Changes limited to formatting, latency, or nonclinical user-interface features may be
appropriately managed within a quality system when they do not alter the meaning,
directiveness, or reliability of outputs. Changes affecting the underlying model, clinical
knowledge sources, retrieval logic, safety guardrails, escalation thresholds, autonomy,
or intended populations may warrant more substantial re-evaluation. FDA should
encourage sponsors to define objective criteria for determining when a change can be
managed within the quality system, when PCCP testing is appropriate, and when a new
premarket submission may be needed.

Foundation Model Device Master Files Could Improve Efficiency if They Are
Current, Standardized, and Useful

The NHC supports FDA’s exploration of voluntary Foundation Model Device Master
Files (MAFs). A well-designed MAF program could reduce duplicative submissions, give
review teams a more consistent understanding of widely used foundation models, and
provide a mechanism for model developers to share confidential safety-relevant
information that individual device sponsors may not otherwise possess. This could be
particularly valuable as more medical devices rely on a smaller number of generalpurpose foundation models.

To be useful, Foundation Model MAFs would need to be versioned and kept current.
Relevant content could include training-data provenance at an appropriate level of
detail, architecture and supported modalities, known limitations and failure modes,
evaluation results relevant to health care, subgroup performance, refusal and escalation
behavior, safety guardrails, update cadence, material-change notification practices,
cybersecurity considerations, audit-log availability, and the circumstances under which

20 U.S. Food and Drug Administration, Marketing Submission Recommendations for a Predetermined

Change Control Plan for Artificial Intelligence-Enabled Device Software Functions: Guidance for Industry
and Food and Drug Administration Staff (August 2025), https://www.fda.gov/regulatoryinformation/search-fda-guidance-documents/marketing-submission-recommendations-predeterminedchange-control-plan-artificial-intelligence.
21 Medical Device Innovation Consortium, Predetermined Change Control Plans (PCCPs) for Artificial

Intelligence (AI) and Machine Learning (ML)-Enabled Medical Devices, September 1, 2024,
https://mdic.org/wp-content/uploads/2024/09/MDIC-AI-ML-Framework-v2.pdf.
NHC Feedback on Regulatory Approach for Generative AI-Enabled Medical Devices
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prior evaluation may no longer apply. The program should also encourage standardized
model or system cards that provide a common structure for this information. The NHC
has previously pointed to model-card frameworks as an example of infrastructure that
can support transparent, reproducible evaluation.22

Voluntary MAF participation should not become a de facto prerequisite that
disadvantages device developers whose foundation-model providers do not participate.
At the same time, the absence of a MAF should not reduce the evidence needed to
support the safety and effectiveness of the final device. The NHC encourages FDA to
preserve sponsor responsibility for understanding and managing safety-relevant
dependencies on the underlying model.

For the MAF program to remain useful over time, FDA should consider expectations for
maintenance and notification. A file that accurately describes a foundation model at
initial submission but is not updated after significant changes may provide limited value
to downstream device sponsors and reviewers. Participating model developers could
identify the versions covered by the file, define material updates, and provide timely
revisions or notices when safety-relevant characteristics change. Standardized fields for
update cadence, deprecation policies, known health care limitations, and availability of
prior versions may also improve the usefulness of MAFs.

Because participation would be voluntary, FDA should also consider incentives that
make MAFs useful to model developers without creating the impression that the
underlying foundation model has been authorized for medical use. Predictable gains in
review efficiency, clearer expectations for information commonly requested across
device submissions, and confidential handling of proprietary technical information may
encourage participation. FDA should also make clear that each sponsor remains
responsible for validating the complete device configuration, including prompts, retrieval
systems, guardrails, interfaces, tools, and the deployment environment.

Agentic GenAI Warrants Additional Risk-Proportionate Safeguards

Agentic GenAI warrants heightened attention because risk can arise from a sequence of
individually plausible actions rather than a single output. An agent may retrieve data,
interpret results, call tools, generate orders, communicate with patients, or control
another device across multiple steps. Small errors can compound, and opportunities for
human review may diminish as the system becomes more autonomous. The NHC
supports FDA’s inclusion of agentic competencies within the proposed benchmarking
framework and encourages additional evaluation of cumulative failure, tool misuse,
permission boundaries, and recovery from intermediate errors.

As an agentic system gains authority to take consequential actions, acceptance criteria
and safeguards should become more stringent. Depending on intended use and risk,
safeguards may include clearly defined permissions and scope, least-privilege access
to data and tools, and meaningful human authorization before irreversible or high22 National Health Council, “NHC Responds to NIH Artificial Intelligence Strategy RFI,” July 15, 2025,

https://nationalhealthcouncil.org/letters-comments/nhc-responds-to-nih-artificial-intelligence-strategy-rfi/.
NHC Feedback on Regulatory Approach for Generative AI-Enabled Medical Devices
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consequence actions. Other safeguards may include reliable detection of erroneous tool
outputs, auditable action logs, fail-safe states, rollback or interruption mechanisms, and
resistance to prompt injection or malicious content introduced through retrieved
information or connected tools. Evaluation should also consider whether the system
recognizes when a planned sequence has moved outside its intended use even if
individual steps appear permissible.

These considerations reinforce the importance of meaningful human oversight. When
human review serves as a safeguard, the reviewer should have sufficient time,
information, authority, and understanding to identify and interrupt an unsafe action.

Agentic systems also raise questions about cumulative risk. A system may make
several individually plausible choices—selecting a data source, summarizing
information, choosing a tool, setting a parameter, and initiating an action—that
collectively lead to an unsafe result. The NHC encourages FDA to evaluate full task
sequences, error propagation, recovery after an incorrect intermediate step, and the
ability to stop when uncertainty exceeds an appropriate threshold. Acceptance criteria
should consider not only the final action but also whether the sequence was auditable,
appropriately constrained, and recoverable.

Human oversight should also be evaluated in the context of the actual workflow. A
requirement for clinician approval may provide limited protection if a system presents
numerous recommendations at high speed, supporting information is unavailable, or the
workflow makes rejection difficult. The NHC encourages FDA to consider whether
oversight is timely, informed, and operationally realistic. For higher-consequence
actions, affirmative confirmation at a meaningful checkpoint may be more appropriate
than relying on the theoretical availability of a reviewer after the fact.

Transparency, Trust, and Patient Engagement Should Remain Cross-Cutting
Priorities

Although the discussion paper appropriately focuses on regulatory science, successful
implementation will also depend on whether patients and clinicians understand and trust
the devices they use. The NHC encourages FDA to preserve transparency as a crosscutting expectation. Patients should have access to understandable information about
when a regulated GenAI function is materially shaping information or decisions, and
what the tool is intended to do. This information should also explain the tool’s
limitations, when human review is available or recommended, and how significant
changes may affect use.23 Technical disclosure alone has limited value if the
information is not usable by the people making decisions.

23 NHC, Principles on Health AI. See Principle 3: Patient Education and Empowerment; Principle 5:

Trustworthiness; Principle 6: Comprehensive Transparency; Principle 9: Human Collaboration; and
Principle 12: Accountability.
NHC Feedback on Regulatory Approach for Generative AI-Enabled Medical Devices
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The NHC has repeatedly emphasized early and sustained patient engagement in AI
development and oversight.24 Patients and caregivers can help identify which errors are
most consequential, what levels of uncertainty may be acceptable, which explanations
are understandable, and what escalation pathways are practical. Meaningful
engagement can inform not only interface testing but also intended use, benchmark
scenarios, communication criteria, postmarket signals, and change-management
priorities.

FDA can make this expectation more concrete by encouraging sponsors to describe
patient engagement in regulatory submissions when it materially informed device design
or evaluation. Relevant information may include which patient and caregiver populations
were engaged, when engagement occurred, the questions considered, how participants
were selected, and what changed as a result. This need not become a rigid procedural
requirement. Rather, documenting how patient input influenced the development
program can help FDA assess whether engagement was timely, fit for purpose, and
relevant to the device’s foreseeable benefits and risks.

Transparency should likewise be tailored to the decision being made and the needs of
the intended audience. A patient may not need detailed information about model
architecture to use a lower-risk tool safely. The patient will still need to understand that
an individualized recommendation is generated by AI, that the system can be wrong,
what information it used, whether a clinician reviewed the output, and how to seek
review or correction. Clinicians may need additional information about validation
populations, known failure modes, changes across versions, and circumstances in
which the system should not be relied upon. The NHC encourages FDA to support
layered communication that makes the most relevant information usable for different
audiences.

These expectations are also consistent with perspectives across the patient and
medical technology communities emphasizing representative evidence, transparency,
meaningful human oversight, postmarket surveillance, privacy, and safe use. These
perspectives reinforce a central point: innovation and patient protection are not
opposing objectives. A predictable, evidence-based, patient-centered framework can
increase confidence in beneficial technologies and help distinguish them from tools that
have not demonstrated reliable performance.25,26

24 NHC, Principles on Health AI. See Principle 1: Patient Health Benefit; Principle 2: Patient-Centered and

Informed; Principle 3: Patient Education and Empowerment; and Principle 13: Ethical Considerations.

25 Alzheimer’s Association, “Technology Safety for Older Adults,” accessed October 6, 2026,

https://www.alz.org/help-support/caregiving/safety/technology-safety-older-adults.
26 Advanced Medical Technology Association (AdvaMed), The Role of Artificial Intelligence (AI) in

Healthcare, November 11, 2024, https://www.advamed.org/member-center/resource-library/the-role-ofartificial-intelligence-ai-in-healthcare/.
NHC Feedback on Regulatory Approach for Generative AI-Enabled Medical Devices
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Conclusion

The NHC appreciates FDA’s decision to seek early public input on the regulation of
GenAI-enabled medical devices. The discussion paper provides a useful foundation by
recognizing that generative systems may require different approaches to risk
assessment, evidence generation, change control, and postmarket monitoring from
those used for traditional software. It also preserves risk-based and least-burdensome
principles. The NHC supports continued development of the proposed risk and
competency-based frameworks, with particular attention to patient context, real-world
comprehension, subgroup performance, clinically meaningful confirmation,
transparency, and clear accountability across the total product lifecycle.

As FDA develops future policy, the NHC encourages the Agency to continue engaging
patients and patient organizations as active partners in defining what safe, effective,
and trustworthy GenAI looks like in practice. A durable framework should support
innovation while maintaining evidence expectations that are proportionate to risk and
responsive to changes in models, populations, and deployment environments. A
patient-centered, fit-for-purpose approach can help beneficial GenAI-enabled devices
reach patients while building the confidence needed for responsible adoption.

Please do not hesitate to contact Jennifer Dexter, Senior Vice President, Policy &
External Affairs, or Shion Chang, Assistant Vice President, Policy & Regulatory Affairs,
if you or your staff would like to discuss these comments in greater detail.

Sincerely,

Randall L. Rutta
Chief Executive Officer