Should it matter whether the clinician using the AI is a generalist or a specialist?

Question 4: Generalist versus specialist use The distinction between generalist and specialist physicians should inform risk when safe use depends on recognizing a specialty-specific error. The relevant question is not simply whether the user is a licensed healthcare professional. It is whether the intended user can independently evaluate the device's output, identify important omissions or contradictions, and prevent harm before the output influences care. Radiation oncology illustrates this problem. A generated recommendation about treatment may appear reasonable while using an inappropriate fractionation schedule, overlooking a previous radiation course, failing to account for cumulative dose in re-irradiation, or applying an organ-at-risk constraint outside the setting in which it is valid. These are not necessarily obvious factual errors. They may be plausible statements whose danger is visible only when the output is reconciled with the full treatment history, disease setting, anatomy, treatment intent, and competing constraints. For functions that extend specialty knowledge to generalist users, FDA should consider three features in addition to the proposed activity and consequence axes: whether the error is independently detectable by the intended user; whether the action is reversible before harm occurs; and whether timely specialist escalation is realistically available. A disclaimer or a generic instruction to consult a specialist is not an adequate safeguard when the output is specific, personalized, and likely to influence action. Risk may be mitigated by a narrow intended use; visible source provenance; explicit identification of missing data; calibrated uncertainty; reliable abstention; specialty-specific escalation criteria; and workflow controls that prevent a high-consequence recommendation from becoming an order without appropriate review. Where safe interpretation depends on specialist knowledge, the device should be benchmarked and clinically confirmed by qualified specialists even when the intended end user is a generalist.

Question 4: Generalist and specialist users Risk increases when safe use needs specialist knowledge and the user does not have that knowledge. The intended user must form part of the intended use. A device for specialist use and a device for generalist use are different devices for evaluation purposes. The manufacturer must confirm each intended use. Controls can include: Newton’s Tree Inc Considerations for the Regulation of Generative AI-Enabled Medical Devices FDA Docket No. FDA-2026-N-7874 Verified user roles. Role-based access. Specialist confirmation for specified actions. Automatic deferral for complex cases. Monitoring of agreement, rejection, and override by user type. The device should use verified role information. It should not estimate a user’s specialty from the conversation.

Response to Discussion Question 4 – Generalist versus Specialist HCPs I agree that the distinction between generalist and specialist physicians may be relevant for Criticality Stratification. However, several caveats should be considered. A specialist may have substantially greater expertise regarding a specific disease, intervention, or diagnostic question. However, a generalist may in some circumstances have a broader understanding of the patient's overall clinical situation, including comorbidities, concurrent treatments, and effects outside one specialty domain. The relevant consideration should therefore be whether the intended user possesses the competence and information necessary to independently assess the particular output, rather than professional designation alone. The intended clinical environment may also be relevant. In specialist departments, narrowly defined workflows and condition-specific protocols can often be implemented more consistently. General practice may involve a much broader spectrum of clinical situations, making comprehensive case-specific protocols more difficult to establish. Accordingly, any criticality-modifying effect should be based on the competence, information, and workflow conditions that can actually be assured for the intended user group rather than on professional designation alone.

Question 4. Generalist versus specialist HCP-facing functions. Three things seem proportionate: labeling that states the expected competency of the intended user explicitly; benchmarking and human factors testing performed with adjudicators and simulated users matching the least specialized intended user (not the most expert available); and referral-prompting behavior, recognizing when specialist involvement is warranted, treated as a tested competency under S.2/E.2 rather than an assumed safeguard. Where these are demonstrated, functions that extend specialist knowledge to generalists can be a meaningful access benefit, particularly in underserved and rural settings.

Question 4 - Generalist versus specialist HCP-facing functions The distinction should be treated as a context modifier rather than a categorical risk label. The relevant issue is whether the intended user can independently evaluate the output within the domain in which the device is operating. A generalist using a specialist-domain tool may benefit substantially from expanded access to expertise, but safeguards should increase when safe use depends on specialist-level interpretation that the intended user is unlikely to possess. Sponsors should therefore characterize the expected user, required domain knowledge, intended degree of independent review, and escalation conditions. Where independent specialist-level review or confirmation is necessary for safe use, that requirement should be reflected in the workflow rather than implied only through labeling. 5

Question 4 — Generalist versus specialist clinician comparators Benchmark against the clinician who would actually perform the task — not against a job title. The appropriate comparator should be defined by the clinical task in its intended environment and set prospectively as part of the device's scope of practice, not assumed from whether the user identifies as a generalist or a specialist.

Question 4 — How should risk be assessed when AI provides specialist-level information to a nonspecialist clinician? Response AI has enormous potential to move knowledge rather than move patients. A generalist should not always need to send a patient elsewhere merely because the relevant expertise is concentrated elsewhere. AI can help democratize specialist-level knowledge and make current evidence available at the point of care. But the safety standard cannot simply be: “The AI gave the generalist access to specialist information.” The important questions are:  Was the information grounded in current evidence?  Did the AI correctly recognize the limits of generalist practice?  Did it identify when specialist involvement was actually necessary?  Did it distinguish between a case that could safely remain with the generalist and one that required escalation?  Did the clinician understand the degree of uncertainty?  Was there an appropriate mechanism for specialist review when needed? The objective should be to move the knowledge, not automatically move the patient—while preserving appropriate escalation when specialized expertise is genuinely required. AI can make expertise more portable. It should not create false equivalence between access to specialist information and actual specialist competency.

Question 4 — Generalist versus specialist users This distinction is analytically real but creates an enforcement problem CDRH should confront directly. A manufacturer can label an intended user; it cannot control which clinician opens the application. Once a device is deployed in an institution, use by clinicians outside the labeled specialty is foreseeable, and in some settings it is the norm. 3 of 19 Docket No. FDA-2026-N-7874 If CDRH intends specialty scope to bear regulatory weight, the paper should distinguish between (a) labeled intended user, (b) technical enforcement of user scope, and (c) foreseeable off-label user, and should state which of the three drives the risk assessment. My recommendation is that risk be assessed against foreseeable use, with credit given for demonstrated technical enforcement — role-based access controls tied to institutional credentialing, for example — rather than for labeling alone. Labeling-only mitigations have a poor track record in software and should not be treated as risk-reducing here.

FDA Question 4 - Generalist vs specialist HCP use Trace ID. TR-Q04 | FDA Q4; Sec. IV.A; App. B; pp. 9-10 / 27-28 BCR response. Base risk on task-to-competency mismatch, not professional title alone. If safety depends on specialty knowledge, validate the generalist's independent review and the device's specialist-escalation behavior. BCR rule basis. BCR-R01,R08,R10,R12 Solution-stack link. S6,S8 Closure evidence. Domain-specific error detection and specialist-escalation testing Pass / re-open. Intended HCP can independently review or device reliably escalates Re-open when: Clinical scope or intended-user change.

Response to Question 4: Generalist vs. Specialist HCP-Facing Applications The distinction between generalist and specialist HCP-facing applications is clinically significant. A generative AI tool designed for use by a board- certified interventional cardiologist carries different risk implications than the same tool used by a primary care physician without specialty training in interpreting its outputs. FDA should address this distinction explicitly in its labeling and intended use requirements, requiring manufacturers to specify the intended clinical user's competency level and to validate performance within that specific user population.