Regulatory Voice and Verb Choice: FDA Benefit–Risk Language Examples for ML Models You Can Adapt

1) Regulatory voice and verb ladders for evidence strength

Regulatory voice is a disciplined way of writing that serves three priorities: clarity, neutrality, and traceability. Clarity means every claim is understandable to a clinician who is not a statistician and to a reviewer who did not run your analyses. Neutrality means you avoid promotional tone and emotional emphasis; you present what the evidence shows without exaggeration. Traceability means every statement can be connected to a defined source of evidence, a method, or a dataset. When you adopt regulatory voice, your language guides the reader from evidence to claim, not the other way around.

For machine learning (ML) software as a medical device (SaMD), regulatory voice becomes especially important because model outputs are probabilistic, context-dependent, and subject to dataset and deployment shifts. The verbs you choose carry the weight of your evidence. This is why you use a “verb ladder”—a graded set of verbs that aligns each claim to the strength of its evidential basis. At the top of the ladder are verbs that indicate robust, replicated, and clinically verified evidence. Lower down are verbs that signal early findings, constrained conditions, or unconfirmed trends.

A helpful way to think about this ladder is to ask two questions before selecting a verb: What is the design strength of the evidence? What is the external validity of the evidence? Design strength refers to study type (e.g., prospective vs retrospective), control of bias (blinding, adjudication), and statistical rigor (pre-specified endpoints, calibration checks, error bars). External validity refers to how well the data represent the intended-use population and environment. If both are high, stronger verbs are justified. If either is limited, choose conservative verbs and add explicit qualifiers.

In regulatory voice, verbs must also signal the limits of inference. For example, “demonstrates” typically implies well-controlled studies with clinically meaningful endpoints and appropriate comparator analyses. “Shows” may be used for strong analytical performance without fully established clinical impact. “Suggests” often signals exploratory or hypothesis-generating findings. “May reduce” or “is expected to” indicates a proposed benefit with mechanisms or preliminary data but lacking confirmatory clinical outcome evidence. Similarly, verbs for risk should be precise about the mechanism: “can misclassify when,” “is sensitive to,” “exhibits drift under,” rather than generic phrasing like “may not work.”

Neutrality also extends to adjectives and adverbs. Avoid intensifiers like “significantly” unless you specify statistical significance and the corresponding measure (e.g., p-values or confidence intervals) in the full technical dossier. Avoid subjective qualifiers such as “robust” or “reliable” unless you define the robustness conditions and reliability metrics. If the evidence is conditional, state the conditions explicitly: “demonstrates improved triage time in emergency department settings using protocol X,” not “demonstrates improved triage time.” Conditional claims are clearer, more honest, and more reviewable.

Regulatory voice requires each claim to be attributable. Use anchor phrases that link to data: “Based on external validation in N sites,” “Using data at prevalence P,” “Under pre-specified threshold T,” “Compared against standard-of-care Y.” These anchors allow reviewers to trace your claim back to specific methods and populations. When you adopt this practice, your verb ladder and your anchors work together: the verb communicates strength; the anchor communicates scope.

2) Mapping benefit–risk–uncertainty to modular sentence frames tailored to ML diagnostics

A modular sentence frame is a reusable structure that encodes the elements regulators expect: context of use, population, comparators, metrics, and limitations. For ML diagnostics, these frames must accommodate probabilistic outputs, thresholds, calibration, prevalence, and decision-support positioning. They are not templates for marketing language; they are scaffolds that ensure completeness and precision.

Begin by mapping the three core content pillars: benefit, risk, and uncertainty. Each pillar has a distinct communicative goal and a distinct set of required details.

• Benefit: The purpose is to connect model performance to a clinically meaningful outcome in a defined context. Benefits in ML SaMD cannot be stated as general “improvements” without specifying the chain from output to clinical decision to patient impact. Frame the benefit to include the intended use (e.g., triage, screening, adjunct to diagnosis), the target population (inclusion/exclusion parameters), the operational settings (inpatient, outpatient, device-specific modalities), and the performance conditions (thresholds, prevalence, metrics). Your frame should explicitly link the model’s function to a clinical decision point, such as reducing unnecessary tests, accelerating specialty referral, or improving rule-out safety.

• Risk: The purpose is to identify plausible failure modes, quantify residual risk after mitigations, and describe clinical consequences in plain terms. In ML, failure modes often stem from dataset shift, calibration mismatch, subgroup performance variability, and workflow misapplication. The frame should name the risk, name the condition under which it manifests, and describe the potential clinical impact without minimizing language. It should also state the current mitigation (e.g., guardrails, alerts, thresholds, human-in-the-loop review) and what risks remain after those mitigations.

• Uncertainty: The purpose is to distinguish known unknowns (recognized limits of evidence) from unknown unknowns (deployment contingencies) and to indicate how you will monitor and mitigate them. In ML, state the source (e.g., limited representation of a subgroup, rare prevalence leading to unstable PPV, site-specific imaging protocols), the scope (which settings or populations the uncertainty applies to), and the plan (post-market surveillance, recalibration triggers, drift detection cadence, user guidance). Uncertainty language should be explicit and operational, not vague. You do not merely “acknowledge” uncertainty; you specify it and link it to an action.

For probabilistic outputs, enrich all three pillars with precise qualifiers. State threshold(s) used for classification, including different operational thresholds if applicable to diverse clinical roles. Provide PPV and NPV at the stated prevalence and note how they change across a plausible prevalence range. Describe calibration behavior: whether predicted probabilities align with observed outcomes across risk strata and whether any recalibration was performed. Position the software as decision support unless it is cleared for stand-alone diagnosis. This positioning helps prevent over-reliance and positions the clinician as the final arbiter.

Modular frames should embed traceability cues. Examples of such cues include: “In [Setting] with [Population], under [Threshold/Workflow], the model [Verb Ladder] [Outcome], relative to [Comparator], with [Metrics] at [Prevalence]. Residual risks include [Named Failure Modes] with [Magnitude/Confidence], mitigated by [Controls], with remaining uncertainty in [Scope] addressed by [Monitoring Plan].” This single frame can be adapted for benefits, risks, and uncertainties by changing the verb, the anchors, and the concluding plan.

Finally, ensure the frames are consistent across documents (labeling, instructions for use, technical summaries). Consistency prevents misinterpretations and reveals honest alignment between analytical validation, clinical validation, and the intended-use description. Your goal is a regulator-ready narrative that a clinician can also understand on first read.

3) Applying the frames: strong vs weak phrasing for ML diagnostics

Although we are not providing full examples here, it is important to internalize the characteristics that distinguish strong phrasing from weak phrasing when you apply the frames.

Strong phrasing aligns verb choice with evidence, specifies context and population, ties metrics to thresholds and prevalence, and translates performance into clinical relevance without overreach. It uses neutral tone, avoids promotional adjectives, and names uncertainties with a plan. Each clause is traceable to a defined dataset or analysis, and every claim can be located in your validation report.

Weak phrasing, by contrast, uses generic verbs that neither match the evidence nor reveal its limits. It speaks in generalities about "accuracy" without stating thresholds, prevalence, or calibration. It conflates analytical performance with clinical utility and leaves out the context of use. It minimizes or omits risk, avoids naming failure modes, or uses euphemisms. It refers to uncertainty vaguely (“may vary”) without stating the source, scope, or monitoring strategy. Weak phrasing often “front-loads” benefits and tucks limitations into isolated disclaimers; this structure undermines trust and invites regulatory concern.

When you apply the frames for benefit statements, strong phrasing will connect performance to a clinical decision point. It will state explicitly how the model’s output is intended to be used (e.g., as a triage flag reviewed by a clinician), and it will include operational details such as threshold settings and how those settings affect sensitivity, specificity, PPV, and NPV across the relevant prevalence. If calibration is central to safe use, strong phrasing will state calibration behavior and the maintenance plan.

For risk statements, strong phrasing will name the technical mechanism and the clinical consequence. For example, if the model exhibits sensitivity loss in older imaging hardware, the phrasing will specify that condition and summarize the clinical implication succinctly. If a mitigation exists (like an automatic device-version check), strong phrasing will include it and clarify any residual risk that remains after the mitigation.

Regarding uncertainty, strong phrasing will separate what is known (quantified limits) from what is unquantified but plausible (areas targeted by post-market monitoring). It will identify subgroup variability, dataset shift risks, and calibration at rare prevalence, and it will integrate a structured plan: thresholds for retraining, triggers for alerting users, and frequency for performance re-estimation.

The intent of applying these frames is not to diminish the model’s value but to present it in a way that is credible, verifiable, and clinically meaningful. By doing so, you reduce the burden on reviewers and support safe and effective adoption.

4) Guided practice: assembling a concise benefit–risk paragraph using frames and a checklist

To translate these concepts into routine writing practice, create a repeatable assembly process that relies on modular frames and a short checklist. The aim is to produce a concise paragraph that captures benefit, risk, and uncertainty without losing critical technical specificity.

Begin with the benefit frame. Insert the intended use (triage, screening, adjunctive decision support), the clinical context (care setting and workflow), the target population (age ranges, comorbidities if relevant, imaging device types or lab methods), and the operational conditions (thresholds, prevalence). Then choose a verb from your ladder that matches the evidence. If you have prospective, multi-site data with clinically relevant endpoints and pre-specified analyses, you may select a stronger verb like “demonstrates” or “shows.” If the evidence is retrospective or limited in scope, opt for “suggests,” “is associated with,” or “may support,” and state the constraint explicitly. Conclude the benefit statement by linking to a clinical decision: what action the clinician might take differently because of the output.

Proceed to the risk frame. Identify at least two meaningful failure modes that persist after mitigations. Name them plainly, specify when they occur, and describe the likely clinical consequence. Avoid minimizing language such as “rarely” unless you can quantify rarity and the conditions under which it holds. State current mitigations (e.g., threshold default, visual confidence aids, required clinician confirmation) and clarify what risk remains despite these. The tone should be matter-of-fact and operational: the reader should be able to anticipate and manage the risk in real workflows.

Add the uncertainty frame. Indicate the source and scope of any unresolved uncertainties, such as limited representation of specific subgroups, emerging variants of disease presentation, or operational changes that could affect prevalence. Connect each uncertainty to a monitoring or mitigation plan: drift detection frequency, recalibration criteria, data quality checks, user alerts, or targeted post-market studies. Emphasize how you will maintain performance and protect patients as conditions evolve.

Finally, integrate probabilistic qualifiers across the paragraph. Specify the operational threshold(s) used, how PPV and NPV behave at relevant prevalence, and what the calibration profile looks like (e.g., well-calibrated in observed ranges, with periodic recalibration). Clearly state the decision-support positioning: whether the model is intended to assist, prioritize, or suggest, and explicitly note when clinician confirmation is required. This positioning clarifies the boundary between model output and clinical judgment, which is central to safe and compliant use.

Use the following concise checklist to verify completeness before submission:

• Voice and verbs
  • Verb matches evidence strength; tone is neutral and non-promotional.
  • Every claim is anchored to a dataset, setting, threshold, and comparator where applicable.
• Benefit content
  • Intended use, context of use, and target population are specified.
  • Clinical decision linkage is explicit; metrics tied to thresholds and prevalence.
• Risk content
  • Failure modes are named with conditions; clinical consequences are stated plainly.
  • Mitigations are listed; residual risks are acknowledged without minimization.
• Uncertainty content
  • Source and scope of uncertainty are identified.
  • Monitoring or mitigation plan is specified with triggers and cadence.
• Probabilistic qualifiers
  • Thresholds, PPV/NPV at stated prevalence, and calibration behavior are provided.
  • Decision support positioning is clear; stand-alone diagnosis is avoided unless authorized.
• Traceability and consistency
  • Claims align with validation documents and instructions for use.
  • Terminology and metrics are consistent across materials.

When you consistently apply this process, you create narratives that are regulator-ready and clinician-friendly. The benefit–risk–uncertainty structure ensures you do not overstate findings or omit necessary cautions. The verb ladder keeps claims aligned to evidence, and the probabilistic qualifiers make the performance interpretable in real clinical contexts. Together, these practices form a disciplined regulatory voice that is both precise and accessible, enabling reviewers and users to understand not only what the ML model does but also how, where, and with what degree of confidence it can support patient care.