RecovryAI Inc.

See attached file(s)

RecovryAI Comments on FDA’s Discussion Paper
Considerations for the Regulation of Generative AI-Enabled Medical Devices

Docket FDA-2026-N-7874 | October 2026 | RecovryAI, Inc.

Framework for Our Responses
These comments draw on our experience developing and prospectively evaluating patient-facing clinical AI through FDA’s presubmission process. We offer four principles and responses to the discussion questions where our experience may be useful. The
principles connect issues that arise across risk assessment, premarket evidence, postmarket monitoring, and modifications to authorized
devices.

RecovryAI has participated in nine FDA pre-submissions. Developing a device that can make defined clinical decisions independently
during post-operative recovery has required us to confront clinical reference standards, prospective evaluation, challenging test cases,
model changes, and ongoing performance monitoring.

Principle 1. Distinguish levels of clinical autonomy by the decisions a device makes independently and
the limits of its validated clinical competency.
Clinical AI can create new clinical capacity when appropriately validated devices independently perform defined clinical work. For
these comments, we distinguish three levels of clinical autonomy:

Clinician-directed AI: AI analyzes clinical information or recommends clinical actions, but a licensed clinician reviews and
approves consequential clinical decisions before they are communicated or carried out.

Protocol-bounded autonomous AI: AI independently makes and communicates clinical decisions within a defined and validated
clinical function, without contemporaneous clinician approval. It escalates situations outside its demonstrated competency or when
clinically meaningful uncertainty remains unresolved.

Broadly autonomous clinical AI: AI independently exercises clinical judgment across a wider range of conditions, presentations,
and clinical decisions without relying on a predefined protocol for each situation. Its clinical authority requires correspondingly
broader evidence.

For example, a protocol-bounded post-operative recovery device may assess symptoms, provide recovery guidance, and determine when
to direct the patient to a clinician. It need not diagnose or manage unrelated medical conditions. Regulatory evaluation is best grounded
in the specific clinical decisions the finished device is authorized to make, rather than the general capabilities of its foundation model.

Principle 2. Match the strength and type of evidence to the clinical decisions the device makes
independently and the potential harm of an incorrect decision.
We see FDA’s two-axis framework as a useful basis for determining how much clinical evidence is needed before a device is
authorized. Prospective clinical studies, retrospective evidence, clinician-adjudicated reference standards, validated benchmarks, and
independent evaluation each contribute different kinds of assurance. As a device exercises greater independent clinical judgment and the
consequences of error increase, we expect stronger premarket evidence to carry greater weight. For lower-risk functions, reliable
postmarket monitoring may support accepting more uncertainty at authorization. We encourage FDA to provide practical examples of
how these evidentiary choices could vary across its proposed framework, while allowing manufacturers to justify alternative methods
appropriate to their intended use.

These approaches are not a fixed hierarchy. A well-designed retrospective study may provide stronger evidence for a particular clinical
claim than a poorly designed prospective study, and independently adjudicated reference data do not necessarily require a separate
testing organization. Postmarket monitoring remains relevant throughout the framework, but it cannot substitute for demonstrating
competency before consequential autonomous decisions reach patients.

Principle 3. Evaluate the finished medical device, including the controls that govern its clinical decisions.
A medical-device manufacturer may use a foundation model developed by a third party without controlling its weights, training data,
architecture, or future updates. The manufacturer remains responsible for validating the complete clinical system, including controls that
define which decisions the device may make, test those decisions against clinical evidence, keep decisions within validated limits,
preserve records of consequential decisions, and monitor performance over time. The appropriate focus of regulatory evaluation is the

RecovryAI – FDA Submission October 13, 2026 (F) 1
finished medical device and its demonstrated clinical performance for its intended use, rather than the underlying foundation model in
isolation.

Principle 4. Consider whether the clinical information available to the device is sufficient to support its
decisions.
Two devices may exercise similar clinical autonomy and pose similar consequences of error while making decisions from substantially
different information. A patient’s description of swelling provides different evidence than a photograph, video, or longitudinal record
with relevant laboratory results and wearable measurements. We do not propose a literal third axis in FDA’s framework. Rather, the
clinical relevance, reliability, detail, and variety of the information available should inform what decision-making competency can be
demonstrated. More data alone does not establish that a clinical decision is valid.

Responses to FDA Discussion Questions
Question 1
Does the two-axis risk framework, organized around device activity and the consequence of relying on an incorrect output,
appropriately capture the dimensions most relevant to the risk of a GenAI-enabled software function? If there are additional
dimensions—such as the reversibility of a resulting action, the availability of downstream safeguards, the time pressure of the
deployment setting, or the traceability of the output (i.e., to primary source materials)—that should be represented in a risk framework,
please describe and provide examples of how they should be represented.
FDA’s two axes provide a useful starting point: greater independent clinical decision-making and greater potential harm call for stronger
evidence and safeguards. Other factors can change the consequences of an incorrect decision. For example, an incorrect
recommendation that is reviewed by a clinician before reaching a patient has a different risk than the same recommendation delivered
directly to a patient. An error that can be corrected before treatment has different consequences than one that causes a missed emergency
referral. Human review is one possible safeguard, but need not remain a permanent requirement for clinical decisions a device has been
validated to make independently.

Question 2
CDRH seeks input on how to account for the spectrum of GenAI-enabled informational functions that vary in the degree to which they
direct a user to a particular action (i.e., between “non-directive” and “action-directing”). What characteristics of an output—such as its
wording, specificity, personalization, or context—could be considered as modifiers of the risk associated with an informational function,
after accounting for the device’s overall functionality and intended use? What additional information would provide manufacturers with
sufficient clarity and predictability around risk assessment for informational functions while recognizing that directiveness may exist
along a continuum rather than as a binary distinction?
The practical question is what clinical action a device encourages or directs, and how likely a patient or clinician is to act on it. “Contact
your surgeon today about increasing calf swelling” is more directive than general information about possible causes of swelling,
regardless of whether either sentence uses the word “recommend.” Examples that compare actual clinical directions, patient
circumstances, and consequences of following an incorrect direction would help manufacturers assess this continuum.

Question 3
CDRH seeks input on whether and when a GenAI-enabled function that results in delivery of clinical information to patients, as opposed to HCPs, could
present different or higher risks, while also recognizing the potential benefits associated with improved patient empowerment, engagement, and access to
clinical information. What device characteristics, output features, or safeguards might mitigate risks that could arise when a user lacks the domain
knowledge to independently evaluate an output, without unnecessarily underestimating patient capability?

Patient-facing delivery alone does not warrant a higher risk tier. The relevant considerations are the consequence of an incorrect output,
the degree of independent clinical authority, and the safeguards around the function. Bounded intended use, limits on the clinical actions
the device may recommend or direct, validated escalation behavior, and evidence appropriate to the consequence of error can support
patient-facing autonomous functions without underestimating patient capability. Human-factors evaluation can also establish whether
patients with different levels of health literacy understand the device's clinical directions and know when to seek additional care.

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Question 4
CDRH seeks input on whether and how the distinction between generalist and specialist physicians might inform the assessment of risk for HCP-facing
GenAI-enabled functions. Under what circumstances, if any, might risk be affected when an HCP who lacks the relevant clinical specialist knowledge
receives information that falls within a specialist area of practice? What device characteristics or safeguards might mitigate such risks?

For HCP-facing functions, the intended user’s expertise can affect whether an error in specialist information will be recognized and
corrected. A generalist receiving a specialist treatment recommendation may require different safeguards from a specialist assessing the
same recommendation. Testing with the intended users and specialist review of the clinical content can help establish those safeguards.

Question 5
For multi-turn conversational GenAI-enabled devices that may migrate from providing “non-directive” information to “action-directing” information
over the course of an exchange, how should risk be assessed across realistic conversational trajectories? How could the intended use of such a device be
characterized when its behavior is emergent across a conversation?

Evaluate multi-turn AI-patient conversations as complete exchanges, including how the device asks follow-up questions, responds to
changing symptoms, and moves from information gathering to clinical recommendations or directions. The intended use needs to
describe the clinical decisions the device may make during the conversation, not only its individual responses. Complete, time-stamped
AI-patient conversations, including patient inputs, follow-up questions, device responses, clinical dispositions, and relevant system
versions, should be preserved with appropriate privacy protections. These records allow consequential clinical decisions to be
reconstructed and investigated if harm occurs.

Question 6
For GenAI-enabled care escalation functions, CDRH is considering how the evaluation may account for both under-escalation and over-escalation. How
could manufacturers characterize and weigh these two directions of error, given that they may not be commensurable and that acceptable trade-offs may
vary by clinical context?

Under-escalation and over-escalation warrant separate performance measures and acceptance criteria matched to clinical context. A
missed emergency referral can cause immediate harm, while unnecessary referrals impose burdens on patients and clinicians and erode
the capacity benefit the device is intended to provide. Aggregate accuracy should not conceal either kind of error.

Question 7
Is the competency-based approach described above, i.e., device benchmarking followed by clinical confirmation, a useful and appropriate framework for
evaluating GenAI-enabled devices?

The competency-based approach is useful, but benchmarking and clinical confirmation are strongest when connected to the same
clinical reference rather than treated as unrelated phases. For consequential autonomous functions, competency is best demonstrated
against qualified human clinical judgment in conditions that reflect actual use.

Question 8
How might the two-axis risk framework described in Section IV be considered within the competency-based approach described above to help determine
the level of evidence needed for a premarket submission?

The two-axis framework can guide evidence requirements: greater independent clinical authority and greater potential harm call for
stronger evidence before deployment. A practical mapping from risk position to expected evidence would give manufacturers greater
predictability while preserving case-specific judgment.

Question 9
Would a benchmarking structure such as the one described above be likely to provide adequate evidence of clinical knowledge, analytic capabilities,
safety behavior, communication, and generalizability to support a reasonable assurance of safety and effectiveness? Are there elements that are missing,
redundant, or inappropriately categorized? Are there externally developed standards that could be leveraged?

The proposed benchmarking structure is directionally useful, but clinical competency must include the ability to stay within validated
scope and escalate when appropriate. A human-established reference standard can identify both clear cases and genuine clinical
ambiguity. Human-factors methods provide a more appropriate evaluation of communication and user comprehension than a benchmark
score alone.

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Question 10
CDRH recognizes that publicly available benchmarking assets may be subject to data contamination, saturation, and limited real-world
representativeness. How should a sponsor establish that performance on a given benchmark predicts safe and effective real-world behavior for the
device’s intended use? What evidence should support the construct validity of a benchmark used to gate device evaluation, and what role should
sponsor-developed benchmarks play given potential concerns around independence and optimization to the test?

A benchmark is most useful when shown to predict clinical performance for the device’s intended use. Sponsor-developed benchmarks
can be appropriate if the cases, clinician reference standards, adjudication methods, and versioning are documented and independently
assessed as appropriate. For clinical decisions with the greatest potential harm, clinician-adjudicated reference cases provide the
strongest basis for the performance standard, including the handling of serious missed conditions. A high aggregate benchmark score
can conceal errors with serious clinical consequences; such errors warrant separate reporting.

Question 11
CDRH is considering that clinical confirmation for a GenAI-enabled device might not require a prospective clinical study in every case, and has
described above a range of approaches of increasing rigor and patient exposure. How might a sponsor select and justify a confirmation approach
tailored to a device’s intended use and proportionate to the device’s risk profile? Are there device types or risk profiles for which one or more of these
approaches would be insufficient or inappropriate? How might the anticipated distribution of real-world inputs be taken into consideration? Are there
other methods of clinical confirmation that might help inform the evaluation of GenAI-enabled devices?

Clinical confirmation can be tailored to the clinical decisions a device makes and the consequences of error. Prospective evaluation
against qualified clinical judgment is especially valuable when a patient-facing device independently asks follow-up questions and gives
consequential clinical directions: the conversation itself determines what information the device receives. Retrospective records may not
capture those changing questions, responses, and decisions, though they can provide useful complementary evidence.

Question 12
How should sponsors achieve statistically meaningful performance measurement for GenAI-enabled devices? Where synthetically generated inputs
supplement real patient data, how should sponsors account for differences between the synthetic and real-world distributions when estimating
performance, and under what conditions, if any, is it appropriate to combine benchmarking evidence and clinical confirmation evidence to support a
single performance estimate?

Statistically meaningful evaluation requires prespecified clinical performance measures, acceptance criteria, and estimates of
uncertainty appropriate to the intended use. Clinically consequential errors, including failures to recognize emergency conditions,
warrant separate analysis so that strong aggregate performance does not obscure serious failures. Separate reporting of benchmarking
and clinical-confirmation results is appropriate when the tests use different patient populations or study designs. Testing can distinguish
routine presentations from rare, complex, or high-consequence clinical cases. It can also evaluate how patients express the same clinical
concern differently, including differences in literacy, language, communication style, incomplete descriptions, and contradictory
information. These clinical and communication variations can test whether the system reaches appropriate decisions consistently.
Pooling results into a single performance estimate is appropriate only when the sponsor can justify how the different datasets represent
expected clinical use.

Question 13
For which clinical domains, device functions, or subpopulations is synthetic data particularly well-suited, or particularly inadequate, as a supplement to
real-world evidence? What safeguards would mitigate the risk that synthetic data generated by models of the same class as the device under evaluation
reproduces the very performance gaps the evaluation is intended to detect, particularly for underrepresented subgroups?

Synthetic data can be particularly useful for testing rare, complex, or high-consequence clinical situations that are inadequately
represented in prospective studies. Our preferred approach is to begin with real patient cases that have been independently adjudicated
by qualified clinicians, then systematically introduce clinical and communication variations. These may include additional
complications, comorbidities, incomplete information, differences in health literacy, and variations in how patients describe their
symptoms.

Modified cases and their expected clinical decisions should undergo independent clinical review, particularly when a modification could
change the appropriate clinical response. Clinician-created cases may supplement this approach where real examples are unavailable,
but require additional scrutiny for clinical plausibility and potential bias. Synthetic cases generated by AI should not rely on that same
AI to establish the correctness of their expected clinical decisions.

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Question 14
For open-ended device outputs, how should performance comparators and acceptance criteria be selected? When a panel of qualified clinicians serves
as the comparator, how should the applicable standard (for example, the standard of care versus the performance of a median clinician in practice) be
defined and justified? Should generalist or specialist physicians be used as a performance standard? When should human-AI team performance, rather
than the device operating alone, serve as the basis for evaluation?

The reference standard should match the clinical claim and intended use. Depending on the function, credible comparators may include
independent clinician adjudication, validated clinical datasets or instruments, and real-world outcomes. When clinicians adjudicate, their
qualifications should match the clinical task, and disagreement among reviewers should be reported rather than concealed in a single
binary label. Acceptance criteria should account separately for errors with serious consequences. Human–AI team performance is the
appropriate comparator when clinician review is part of the intended use; independently acting devices need evidence of their own
clinical performance.

Question 15
Are there ways in which performance might be assessed relative to the care, technology, or course of action likely to occur in the absence of the device,
rather than to the comparators described in this section? What approaches might be used to identify and justify a comparator such as unaided clinical
judgment, delayed specialist review, or no intervention?

Alternative comparators can be useful, but the comparator needs to match the claim. Retrospective or counterfactual comparison to
usual care may support utilization or workflow claims; concordance with qualified clinical judgment supports claims about the
appropriateness of the device’s clinical decision. A utilization comparator alone cannot establish the clinical appropriateness of a triage
decision.

Question 16
Should independent third parties be involved in some or all aspects of a competency-based approach, including device benchmarking and clinical
confirmation? If so, in what ways might qualified, independent third-party participation contribute to this approach, and what qualifications and
independence criteria should apply? Are there aspects of the assessment for which third-party involvement would be impractical or inadvisable? What
safeguards or program design features would be critical to prevent third-party participation from limiting competition or preventing innovation?

Independent third-party participation can improve confidence in evidence when qualified reviewers independently select or adjudicate
clinical cases and evaluate performance. Credible independence depends on relevant clinical qualifications, control of the evaluation
process, and disclosure of conflicts of interest. A requirement to use a single third-party certification body or testing provider may limit
competition and innovation.

Question 17
CDRH is exploring whether a competency-based approach could be applied to devices that incorporate a variety of underlying model architectures, such
as multimodal vision-language models and generative or predictive world models. Are there aspects of the described approach that might be ineffective
or inapplicable to these devices? What additional or distinct considerations might need to be addressed?

A common competency-based approach can apply across model architectures, with evidence tailored to the clinical information each
device actually uses. Changing text entry to voice, while retaining the same clinical content and decision function, may chiefly require
testing transcription reliability and human factors. Adding photographs or video to evaluate swelling or mobility introduces new clinical
information and calls for validation of that assessment and its effect on the final decision. The relevant question is whether a new
architecture or input changes the clinical function or creates new failure modes, not the model label.

Question 18
CDRH is considering whether it may be appropriate to accept greater premarket uncertainty regarding a GenAI-enabled device’s benefit-risk profile
through greater reliance on postmarket monitoring. Under what conditions might such an approach be appropriate, and what characteristics of a
monitoring program would need to be in place to justify reduced premarket evidence? Are there device types or risk profiles for which this approach
would not be appropriate?

FDA’s two-axis framework offers a useful basis for determining acceptable premarket uncertainty. Devices with limited independent
clinical decision-making and a low potential for harm may reasonably rely more on postmarket evidence, provided problems can be
detected and corrected promptly. As a device makes more consequential clinical decisions independently, stronger evidence is needed
before deployment. Postmarket monitoring cannot adequately substitute for premarket demonstration of competency when an incorrect
decision could cause immediate or irreversible harm, such as failure to direct a patient with emergency symptoms to urgent care.

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Question 19
Please comment on the potential approaches to postmarket performance evaluation, including periodic re-benchmarking, sample-based clinician review,
and performance degradation monitoring. What additional approaches should CDRH consider, and how should the cadence and triggering events for
reassessment be determined?

The premarket competency assessment offers a useful baseline for postmarket evaluation. A fixed, versioned clinical reference set can
support repeatable testing, alongside periodic clinician review and monitoring of consequential errors. An evaluation mechanism that is
separate from the model making the clinical decisions can detect changes without allowing the device simply to grade itself; this need
not involve an outside institution. Reassessment can be periodic and triggered by changes in the model, prompts, clinical controls, or
observed performance.

Question 20
Please comment on whether the proposed approaches to postmarket monitoring can be facilitated by machine-based supervisory agents. What
considerations, including the evaluation and reliability of the supervisory agent itself, should CDRH take into account for such an approach?

Machine-based supervision can support postmarket monitoring if the supervisory method has itself been tested against an independent
clinical reference and its limitations are known. Where feasible, a deterministic or separately validated monitoring process reduces the
risk that the monitor repeats the device’s own errors. Changes to the supervisor also require controlled evaluation. Clinically significant
exceptions should remain reconstructable and available for human review.

Question 21
What roles might clinicians, healthcare institutions, professional societies, standards-setting bodies, and other stakeholders appropriately play in
postmarket monitoring, and how can these roles be structured without diffusing manufacturer accountability?

Clinicians and healthcare institutions can identify and report adverse events and recurring patterns of error. Professional societies and
standards organizations can help develop common definitions, reference cases, and evaluation methods. Independent clinical or
technical review of serious incidents may also identify lessons useful across manufacturers. These contributions would complement, not
transfer, the manufacturer’s responsibility for investigating, reporting, correcting, and monitoring its own device.

Question 22
CDRH envisions that a manufacturer’s premarket competency-based assessment might serve as a baseline against which post-deployment modifications
could be re-evaluated. How might the extent of re-benchmarking or other evidence be scaled to the nature and expected impact of a given modification?
For example, are there categories of change that might not significantly affect safety or effectiveness of a GenAI-enabled device, or might be
appropriately managed within a sponsor’s quality management system versus requiring FDA premarket review and authorization before
implementation? Are there categories of changes that might be appropriate for inclusion in a PCCP?

The evidence required for a modification can follow its likely effect on validated clinical decisions and safety. Changes to clinical
reasoning, escalation criteria, supported conditions, or patient populations may call for additional clinical testing, or implementation
under an authorized PCCP where covered. A change in foundation model need not automatically require a new clinical study if testing
demonstrates that the device continues to meet its established clinical acceptance criteria. Changes such as navigation, screen layout, or
presentation of existing clinical guidance may be addressed through targeted human-factors testing and quality-system controls when
they do not change the clinical function. A PCCP can establish methods and acceptance criteria for defined categories of future
modifications.

Question 23
How might PCCP concepts or other change-control approaches be adapted for GenAI-enabled devices when the nature or scope of future modifications
cannot be fully prespecified?

PCCPs for GenAI can prespecify the boundaries, methods, evidence, and acceptance criteria for classes of foreseeable change even
when the exact future implementation cannot be named. The plan can define what clinical behavior may change, how continued
competency will be demonstrated, and what triggers escalation beyond the PCCP. It can also permit predefined expansion within a
demonstrated competency framework when the sponsor meets prespecified evidence and acceptance criteria, rather than requiring every
future implementation to be known in advance.

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Question 24
For devices built on third-party foundation models, changes to the underlying model may be initiated by the third-party model developer
rather than the device manufacturer. How can a manufacturer detect, evaluate, and respond to such changes in a timely manner? What
mechanisms—for example, contractual, technical, or through a PCCP—could provide reasonable assurance that third-party developerinitiated changes do not compromise the safety or effectiveness of the device?
Manufacturers relying on third-party foundation models need technical methods for detecting and evaluating changes, beyond
contractual notification. Versioned clinical reference datasets, repeated regression testing, and independent monitoring of the complete
medical-device system can help establish whether a model change has affected validated clinical performance.

Because earlier model versions may no longer be available, and individual failures may not be reliably reproducible, the response to a
model change should focus on demonstrating continued clinical competency rather than identifying and reversing every underlying
cause. When an available replacement model cannot demonstrate acceptable clinical performance, manufacturers need a defined
contingency for maintaining patient safety, including transitioning clinical decisions to appropriate human oversight until competency
can be re-established.

Question 25
Would voluntary Foundation Model MAFs be practical for foundation model developers to provide and to keep current, and what content might they
include? Given that participation would be voluntary and that model developers may have limited incentive to disclose safety-relevant information, what
would make such a program sufficiently useful for premarket review? Are there alternative mechanisms CDRH should consider for obtaining
information about underlying foundation models?

Voluntary Foundation Model Master Files could reduce duplicative review by providing version-specific information, known
limitations, safety-relevant evaluations, and change notifications. Responsibility for device safety and effectiveness would remain with
the medical-device manufacturer. Participation should remain genuinely voluntary: the absence of a Master File ought not create a
procedural delay or substantive disadvantage for a sponsor that can independently establish the safety and effectiveness of its finished
device. A predictable alternative pathway would preserve access to qualified models whose developers do not participate.

Question 26
Are there additional considerations that inform the premarket and postmarket evaluation of agentic GenAI-enabled devices, beyond those applicable to
non-agentic GenAI-enabled devices? How should the elevated risk associated with autonomous multi-step action, tool use, and reduced opportunity for
human review be reflected in acceptance criteria and oversight?

The term ‘agentic’ alone is not a sufficient basis for determining regulatory requirements. Evaluation can focus on what the device can
do without human approval, including whether it can use external tools, contact patients or clinicians, initiate orders, or control another
medical device. Evaluation needs to cover the full sequence of actions, the clinical consequences of error, whether an action can be
reversed, and where intervention is possible. Higher-consequence actions may require human approval unless the manufacturer
demonstrates that the device can perform those specific actions safely and independently.

Submitted by RecovryAI, Inc.

RecovryAI – FDA Submission October 13, 2026 (F) 7