Bite Size Ignite Inc. (Narasimham Gubbala)

Bite Size Ignite, Inc. respectfully submits the attached comments addressing FDA Questions 1, 10, 14, 15, 19, 21, 24, and 26, focusing on organizational risk, human–AI collaboration, real-world performance, lifecycle monitoring, and accountability.

Public Comments on FDA's Considerations for the Regulation of Generative AI-Enabled
Medical Devices
Docket: FDA-2026-N-7874
Submitted by: Bite Size Ignite, Inc.
Date: October 19, 2026
Contributors:
Narasimham "Nara" Gubbala, PhD, PMP, PfMP
Founder, Bite Size Ignite, Inc.
Pharmaceutical and Biotechnology Program Execution, Portfolio Governance, and Risk Management
Phillis S. Gubbala, PhD
Bite Size Ignite, Inc.
Biochemistry, Clinical Development, and Regulatory and Scientific Documentation
Introduction
Bite Size Ignite, Inc. appreciates the opportunity to comment on FDA's August 2026 discussion paper
concerning generative AI-enabled medical devices.
Our perspective draws on experience in pharmaceutical and biotechnology development, clinical
operations, cross-functional governance, and regulatory documentation, alongside ongoing
conceptual research into predictive execution intelligence.
We support risk-proportionate evaluation across the total product life cycle and encourage FDA to
consider an additional, cross-cutting principle:
AI-enabled device safety and effectiveness depend not only on technical performance but also
on how device outputs interact with organizational dependencies, human decisions, clinical
workflows, and accountability structures.
Individually appropriate actions and compliant processes do not necessarily ensure reliable systemlevel execution. Risks may emerge at organizational interfaces through delayed decisions,
fragmented responsibilities, or ineffective escalation.
We offer the following recommendations in response to Questions 1, 10, 14, 15, 19, 21, 24, and 26.
Q1 — Risk Assessment: Organizational and Workflow Modifiers
FDA's proposed framework appropriately considers device activity and the consequences of incorrect
outputs. We recommend incorporating organizational and workflow factors as contextual risk
modifiers, rather than necessarily creating additional regulatory risk categories.
Relevant factors include decision latency, clarity of responsibility, cross-functional dependencies,
reversibility of actions, availability of human intervention, and traceability of decisions.
For example, an appropriate device-generated warning may fail to support timely care if responsibility
for acknowledgment or escalation is unclear.
Risk assessment should consider whether the intended deployment environment can reliably
translate safety-critical outputs into appropriate actions.
Q10 — Benchmark Validity and Real-World Performance
We recommend evaluating whether benchmarks represent the operational conditions under which a
device will actually be deployed, including incomplete information, workflow interruptions, changing
responsibilities, and delayed human responses.
High benchmark performance does not necessarily establish equivalent performance within complex
clinical workflows.
Where early warnings are part of a device's intended function, evaluation should distinguish accuracy,
calibration, warning lead time, and actionability.
We encourage prospective or temporally separated validation against appropriate benchmarks and
real-world comparators, with evidence that proposed indicators provide incremental value beyond
established monitoring methods.
Q14 — Human–AI Team Performance
Where a device is intended for clinician-supervised use, evaluation should consider the performance
of the human–AI team as well as the device itself.
Relevant measures include correct interpretation of outputs, recognition of uncertainty, appropriate
overrides, timeliness of escalation, and successful completion of safety-critical actions.
Evaluation should also examine automation bias and whether human oversight remains effective
under realistic workload and time constraints.
Device-level and human–AI team results should be reported distinctly to clarify the contribution of the
technology and the influence of deployment conditions.
Q15 — Comparison with Existing Practice
We support comparing AI-enabled devices against the care or actions reasonably expected without
the device.
Depending on intended use, relevant comparators may include existing clinical workflows, unaided
professional judgment, or current monitoring and escalation processes.
Assessment should distinguish improvements in technical accuracy from improvements in decision
timeliness, workflow execution, and patient-relevant outcomes.
Where an AI-enabled device is intended to accelerate recognition or intervention, the timing and
consequences of actions should be evaluated alongside conventional performance metrics.
Claims of clinical or operational benefit should be supported by evidence appropriate to the intended
use.
Q19 — Post market Performance Monitoring
We support FDA's consideration of periodic re-benchmarking, independent clinician review, and
performance degradation monitoring.
For devices whose safety or effectiveness depends on downstream workflow actions, we recommend
evaluating additional operational indicators where feasible, including:
 • Time from safety-critical output to acknowledgment and action.
• Unresolved or repeatedly escalated alerts.
• Changes in override patterns and workflow exceptions.
• Recurrent failures at system or organizational interfaces.
These indicators should be treated as investigation triggers, not automatically as evidence of device
failure.
Monitoring programs should establish risk-proportionate thresholds, escalation ownership, and
mechanisms for evaluating whether operational changes affect device safety or effectiveness.
Importantly, proposed early-warning indicators should be evaluated for predictive value, false-alert
burden, and practical actionability.
Q21 — Shared Ecosystem Responsibility
Shared responsibility should not result in diffused accountability.
Manufacturers retain applicable regulatory responsibilities, while healthcare institutions and other
stakeholders may contribute information essential to detecting and responding to risks arising during
deployment.
We recommend clearly defined responsibilities for safety-related information exchange, escalation,
investigation, and corrective action, supported by traceable decision records.
Particular attention should be given to risks emerging between organizational boundaries, where
individual stakeholders may fulfill their assigned obligations while system-level coordination remains
inadequate.
FDA should also consider the operational feasibility and resource requirements of monitoring
responsibilities assigned to deploying institutions.
Q24 — Third-Party Foundation Model Changes
Changes initiated by third-party foundation model developers may affect medical-device performance
without originating within the device manufacturer's organization.
We recommend evaluating both technical change controls and the organizational mechanisms
needed to respond effectively.
These may include contractual notification requirements, model-version traceability, defined
responsibility for impact assessment, risk-based re-benchmarking triggers, escalation timelines, and
rollback or deployment-suspension procedures when appropriate.
A key consideration is not merely whether changes can be detected, but whether responsible parties
can assess and respond to potentially safety-relevant changes within an appropriate timeframe.
Q26 — Agentic AI and Multi-Step Autonomy
Agentic systems introduce risks that may emerge from sequences of individually acceptable actions,
particularly when tools, organizations, and human decision-makers interact.
We recommend evaluating end-to-end workflow behavior, including dependencies between actions,
cumulative consequences, authorization boundaries, and opportunities for effective human
intervention.
Testing should include realistic scenarios involving delayed information, failed handoffs, unavailable
reviewers, conflicting instructions, and external system failures.
For high-consequence actions, acceptance criteria should address safe interruption, human
authorization, traceability, and recovery where feasible.
The central question is whether the complete system remains within its intended operating
boundaries throughout multi-step execution—not solely whether individual outputs are technically
correct.
Closing Recommendation
We encourage FDA to consider organizational and workflow-level risk indicators as potential
complements to technical benchmarking and clinical evaluation, particularly for AI-enabled devices
embedded in complex clinical environments.
A valuable research priority is determining whether operational, coordination, and decision-process
signals can identify emerging safety risks earlier than established approaches while providing
sufficient time for meaningful intervention.
Such predictive capabilities should be empirically validated, with prediction, causation, actionability,
and demonstrated improvements in outcomes treated as distinct evidentiary questions.
These recommendations are intended to inform evaluation of FDA-regulated device functions within
their intended use and do not presume that all healthcare AI workflows fall within FDA's device
oversight.
Bite Size Ignite, Inc. appreciates FDA's leadership in advancing scientifically grounded, riskproportionate approaches to the safe and effective use of generative AI-enabled medical devices.
Respectfully submitted,
Narasimham "Nara" Gubbala, PhD, PMP, PfMP
Founder, Bite Size Ignite, Inc.
Phillis S. Gubbala, PhD
Bite Size Ignite, Inc.