Abstract
Generative artificial intelligence (AI) in clinical communication, including in medical imaging, presents behavior-mediated safety challenges: outcomes depend on how clinicians verify AI-generated content under time constraints. However, guidance has largely focused on predeployment validation, with less specificity about postdeployment governance in day-to-day workflows. This perspective synthesizes evidence on failure modes, automation bias, and implementation monitoring and proposes a practical framework organized around three target behaviors: trust (transparent scope limits), verify (structured cross-checks against source data), override (documented corrections that become learning signals). Drawing on behavior change and implementation science, we translate postdeployment risks into stakeholder-specific interventions, including competency-based education, equity-stratified monitoring with prespecified triggers for fairness drift, and rollback procedures. The framework extends to patient-facing AI-generated explanations, where comprehension and autonomy must be safeguarded. This approach positions governance as a health education and behavior challenge essential for safe, equitable adoption.
Keywords
Introduction
Generative artificial intelligence (GenAI) is entering medical imaging through everyday workflows. In many organizations, it functions as a documentation assistant: drafting reports, summarizing prior studies, and producing patient-facing explanations. Unlike traditional artificial intelligence (AI) tools, GenAI introduces behavior-mediated risks, where safety often hinges on how clinicians handle fluent text under time pressure. Do they verify it? Do they escalate discordant outputs? Do they document when they override it?
Those questions point to the core challenge: GenAI is not just a model to be validated but also a behavioral intervention delivered through workflow interfaces. Governance, therefore, must be specified as behavior change techniques (BCTs) paired with implementation strategy bundles, not model evaluation alone. In practice, governance means designing, measuring, and reinforcing reliance behaviors: what clinicians accept, verify, override, and escalate when the workflow is moving rapidly. Medical imaging is a useful sentinel case because its digitized, time-critical workflows make these behaviors visible and measurable early. Because GenAI drafts action-driving text, governance must shape reliance behaviors in real workflows, including patient-facing communication.
Drawing on narrative synthesis of evidence on GenAI failure modes, automation bias, and postdeployment monitoring, this perspective addresses four recurring risk pathways, translating governance principles into measurable practice organized around trust, verify, override. The analysis integrates the Capability, Opportunity, Motivation–Behavior (COM-B) model (Michie et al., 2011) to specify target safety behaviors, the Consolidated Framework for Implementation Research (CFIR; Damschroder et al., 2009) to identify contextual determinants, and the Reach, Effectiveness, Adoption, Implementation, and Maintenance (RE-AIM) framework (Glasgow et al., 1999) to structure pragmatic evaluation.
While individual elements appear in prior work, no governance guidance for clinical GenAI has integrated BCT-specified reliance behaviors, equity-primary fairness drift triggers with prespecified thresholds, and RE-AIM-structured evaluation tied to governance action into a single auditable package. We advance a testable proposition: this package will increase appropriate reliance behaviors and reduce automation bias errors compared with governance focused on model performance alone.
Behavior Mechanisms in AI-Mediated Clinical Communication
Postdeployment safety depends largely on a small set of observable clinician behaviors: verifying AI outputs against source data, escalating discordant or out-of-scope cases, and documenting overrides as learning signals.
Generative AI can alter these behaviors through two primary mechanisms. First, fluent outputs may be perceived as more accurate than warranted, fostering uncritical acceptance (S. S. Y. Kim et al., 2025; Reber & Schwarz, 1999). Second, reduced effort may encourage faster decisions with fewer verification steps (Lyell & Coiera, 2017; Reber & Unkelbach, 2010). These tendencies can erode the safeguards that clinical judgment provides.
These dynamics reveal workflow points where behavior can be intentionally shaped. The COM-B framework offers a structure for intervention: (1) Capability, through verification and calibration training; (2) Opportunity, through embedded supports such as structured prompts and lightweight documentation; and (3) Motivation, through uncertainty cues and error feedback (Michie et al., 2011).
Four Recurring Risk Pathways
GenAI-related harms cluster around predictable interactions between fluent systems, constrained workflows, and human cognition. Four pathways recur and should inform governance design.
Hallucination and Unsupported Assertions
GenAI can produce fluent but clinically incorrect statements, including action-driving errors (wrong problems, wrong timelines, wrong follow-up, wrong certainty; Asgari et al., 2025; Butler et al., 2024; Park et al., 2025; Rao et al., 2024). Prompt engineering can reduce but not eliminate these errors (Anh-Hoang et al., 2025; Cheng et al., 2025). The central risk is detectability: fluent text feels credible, reducing the friction that normally triggers source-checking (Reber & Unkelbach, 2010).
Automation Bias
Clinicians may overweight confident algorithmic suggestions while underweighting their own judgment, a pattern consistent with automation bias (Dratsch et al., 2023; Goddard et al., 2012). GenAI drafts may omit or confabulate information (Song et al., 2025), and fluent phrasing can create a certainty illusion that discourages verification (Sun et al., 2023).
Performance Drift and Fairness Drift
Real-world performance shifts as workflows, documentation norms, staffing, and patient mix evolve. Drift may appear as new error patterns, unstable summarization, or weaker fidelity to source-of-truth data. Fairness drift, defined as widening subgroup gaps that may be masked by stable overall metrics, should be treated as a distinct governance failure mode (Davis et al., 2025).
Workflow Integration Failures
GenAI can amplify the workflows it enters. Poor interfaces and ad hoc integrations that do not scale, along with weak auditability, can increase operational burden and the likelihood of unanticipated problems as AI is deployed across workflows (Tejani et al., 2024). Conversely, designs that make user corrections visible (e.g., capturing override reasons) can convert edits and rejections into learning signals for monitoring and improvement (Aaron et al., 2019).
These pathways define what postdeployment governance must control: how scope boundaries are made visible, how verification is prompted, and how overrides are captured as learning signals for monitoring and improvement.
Trust, Verify, Override: A Postdeployment Governance Heuristic
We propose trust, verify, override as a behavioral governance heuristic for AI-mediated clinical communication. The goal is observable reliance: bounded use, structured verification, and accountable overrides, designed using BCTs and implemented through strategy bundles.
For replicability, we specify each component using recognized BCTs from the BCT Taxonomy (Michie et al., 2013). Trust maps to environmental restructuring and prompts/cues; verify to action planning with a verification checklist and brief attestation; and override to problem-solving steps, reason-coded documentation, and audit-and-feedback (Michie et al., 2011) delivered within a just-culture framework that emphasizes learning over blame (Reason, 2000).
Five supporting tables operationalize each component: sentinel indicators and triggers (Table 1), stakeholder behavioral targets (Table 2), an education blueprint (Table 3), governance checklists with rollback criteria (Table 4), and RE-AIM evaluation designs (Table 5).
Proposed Sentinel Indicators for Behavioral Governance: Early-Warning Signals and Example Triggers Requiring Local Validation.
Note. These sentinel indicators are hypothesized early-warning monitoring targets intended to support learning during rollout; they are not safety standards. Metrics, thresholds, and actions should be locally defined and prospectively validated for sensitivity/specificity and feasibility.
Behavioral Targets, Determinants, Policy Levers, and Measures by Stakeholder Group.
Note. Determinants are framed using the COM-B model (capability, opportunity, motivation). CFIR and RE-AIM inform implementation and evaluation planning.
Tiered Education Blueprint for GenAI Safety in AI-Mediated Clinical Communication.
Governance Checklist for Postdeployment GenAI Use in AI-Mediated Clinical Communication.
Note. Thresholds and triggers are illustrative and should be adapted to local context, risk tolerance, and data availability.
Pragmatic Evaluation Designs Mapped to RE-AIM Outcomes.
Note. Outcomes are organized using RE-AIM (Reach, Effectiveness, Adoption, Implementation, Maintenance). Metrics, thresholds, and response actions are presented as examples and should be locally defined and, where feasible, prespecified to fit the use case, risk tolerance, and available data.
Trust (Bounded Use, Visible Constraints)
Trust is not a feeling. It is constrained authorization. Health systems should define and publish a narrow initial scope (task, population, workflow step) and make those limits operationally explicit to users, including clear versioning, limitations, and ‘not for’ conditions at the point of use (Geis et al., 2019). When clinicians cannot see boundaries, they cannot reliably enact boundaries, and reliance becomes accidental rather than governed.
Verify (Structured Cross-Check Before Sign-Off)
A brief, standardized verification routine combined with accountability cues can reduce automation bias by shifting from passive acceptance to active confirmation (Dratsch et al., 2023; Skitka et al., 2000). Before sign-off, clinicians cross-check GenAI outputs against source-of-truth data, prioritizing high-risk claims, action-driving statements, and any content that could alter triage, follow-up, or patient understanding.
Override (Accountability With Learning Signals)
When outputs are discordant, uncertain, or out of scope, clinicians should edit or reject them and record a reason code (e.g., hallucination, overconfidence, incorrect comparison, out of scope). Track override rates and reasons by setting and subgroup. If only some patients benefit from rigorous verification, equity has already failed.
Operationalizing the Framework
The proposed package includes auditable governance routines, competency-based education for verification and escalation, and pragmatic evaluation for emerging safety and equity risks. The following sections address each in turn.
Stakeholder-Aligned Governance
In our view, postdeployment governance succeeds when the safer action is also the easier action. That requires clarity about who is responsible for which behaviors and supports. Table 2 translates the trust, verify, override heuristic into stakeholder-specific target behaviors, likely implementation determinants (using the COM-B model), policy levers, and measures. If verification is required but time is not protected, or escalation is encouraged but reporting is burdensome, workflows tend to drift toward unsafe behavior (Lyell & Coiera, 2017). CFIR’s inner-setting constructs (implementation climate, readiness, available resources) informed the institutional determinants and policy levers in Table 2.
Education
Education bridges governance intent and frontline behavior. Generic AI literacy is insufficient (K. Kim et al., 2024) because the most consequential failure modes are behavior-specific: verifying claims against sources, communicating uncertainty (Bragazzi & Garbarino, 2024), and escalating or overriding when outputs appear inconsistent (Adams et al., 2025). Table 3 presents a tiered education blueprint linking learning objectives to concrete workflow behaviors, delivery options, and assessment methods. Training should include cases where output is correct but still requires confirmation and cases where output is subtly wrong but fluent; otherwise, concerns will be suppressed, and learning will stall.
Auditable Governance Artifacts
Governance should produce routine, auditable artifacts that answer three questions: What did the model do? What did the clinician do with it? What happened next? Table 4 outlines minimum routines and thresholds, including scope and versioning documentation, sentinel monitoring, incident learning, and explicit rollback procedures. Without prespecified triggers, monitoring becomes purely retrospective. Rollback must be operational, with named owners, defined thresholds, and a review calendar, not a theoretical safeguard. If an organization cannot pause or restrict use when safety or equity signals worsen, its governance is only nominal.
Patient-Facing Explanations
These principles extend to the most visible output of generative AI: what patients read. Patient-facing explanations shape what patients understand, worry about, and do next. Accordingly, AI-generated or AI-assisted explanations should be clearly labeled, written to invite questions rather than convey certainty, and evaluated for comprehension and unintended anxiety (World Health Organization, 2024). As a pragmatic target, explanations should use plain language for broad readability (DeWalt et al., 2011) and include a brief comprehension check such as teach-back (Talevski et al., 2020) or brief chunking with confirmatory checking (DeWalt et al., 2011).
Pragmatic Evaluation
Governance and education should be paired with feasible, decision-oriented evaluation that prioritizes learning and safety over perfect causal inference. Table 5 maps pragmatic evaluation designs to RE-AIM outcomes (Glasgow et al., 1999), with example measures and governance decisions each design informs.
Two principles guide this approach. First, pair automated signals with targeted audit: logs can show when and how GenAI was used, but expert review is often needed to determine whether edits reflect error correction, stylistic preference, or training needs. Second, tie metrics to action: each measure should prompt a specific governance decision.
Applying the Heuristic Beyond Imaging
Imaging is a useful sentinel case, but the trust, verify, override heuristic applies wherever GenAI drafts health content, including community health worker visit summaries, discharge instructions, behavioral intervention prompts, and patient portal explanations. The source of truth changes, but the governance logic does not.
Verify means the responsible author cross-checks action-driving claims before distribution for guideline alignment, patient constraints (language, contraindications, access barriers), and clarity about next steps. A ‘verified against: ___’ annotation keeps this traceable.
Override means intentionally correcting or rejecting output that could misdirect behavior (incorrect eligibility criteria, wrong referral pathway, culturally mismatched framing, or unsafe triage advice) and recording a reason code (e.g., policy mismatch, missing patient constraint, safety concern, tone/cultural fit) so corrections become improvement signals rather than one-off edits.
Equity monitoring translates directly: track comprehension and follow-through (teach-back results, referral completion) by language and other locally prioritized groups, and narrow or pause generation when subgroup gaps widen.
Implementation Considerations and Unintended Consequences
Implementation is not primarily a technical capacity problem; it is a behavioral systems problem. Every deployment should include a minimum behavioral package: (1) clearly declared scope boundaries that reduce inappropriate trust, (2) a verification routine that is teachable and auditable, and (3) an override pathway that is psychologically safe and operationally fast. Sites with fewer resources can scale the tooling, but they cannot omit the behavioral minimum without accepting predictable safety and equity failures.
Several unintended consequences deserve attention. Verification can be resource-intensive, especially when it relies on manual steps or retrospective review, increasing operational burden and limiting scalability (Chow et al., 2025). Complex interfaces and added supervisory demands can dilute perceived value and strain workloads when clinician capacity is already tight (Brady et al., 2024). Safeguards designed as hard-stops or rigid control rules can backfire by reducing usability and receptivity (Poly et al., 2020). Equity monitoring is ethically important (Embi, 2021), but data-intensive monitoring may feel like surveillance unless it is transparently governed and clearly framed as quality improvement (Muller et al., 2025). Health systems should track workload and trust alongside safety indicators and adjust routines when burden or distrust undermines safe use.
These unintended consequences raise ethical questions that governance must confront directly. Patients receiving AI-generated explanations may not understand what that means for reliability, raising concerns about meaningful informed consent. Logging clinician verification and override behavior is necessary for learning but creates surveillance risk if not governed by transparent, jointly developed policies. And when verification burden falls on those with the least time, accountability becomes inequitable: the organization claims human oversight while the conditions for exercising it are unevenly distributed. Governance design should make these tensions explicit and subject to periodic review rather than treating them as resolved by policy language alone.
One gap this framework does not resolve is vendor accountability. Deploying health systems should require, through procurement and service-level agreements, that vendors accept structured adverse-event reports, disclose version changes before local revalidation is needed, and maintain model cards with known limitations. Override signals and fairness drift findings should flow back to vendors as improvement data, not remain siloed within deploying organizations.
Limitations and Boundary Conditions
The evidence base for postdeployment GenAI safeguards remains uneven, consisting largely of conceptual frameworks and simulation studies rather than multi-site empirical evaluations. Accordingly, several recommendations here should be read as ethically grounded best practices rather than a definitive hierarchy of interventions with known effect sizes.
Generalizability is limited at the level of implementation details and expected effects. While the core governance behaviors generalize beyond imaging, other contexts differ in task structure, feedback loops, and outcome visibility, requiring adapted thresholds and monitoring cadence. Equity monitoring adds constraints: small subgroups may yield unstable estimates, requiring pooled data and sentinel case review. Implementation also involves trade-offs: logging, auditing, training, and monitoring require infrastructure and staffing; tighter controls can add friction; and resource constraints may concentrate residual harm in settings least able to absorb it.
Future Directions
Empirical research on postdeployment governance for GenAI in medical imaging is still emerging, and we present this framework as a set of testable hypotheses. Key priorities include prospective evaluation of governance routines and training programs to assess whether they reduce error propagation and improve equity, and development of sensitive sentinel indicators for each risk pathway.
Conclusion
GenAI will not be safe or equitable based on technical performance alone. Its real-world safety and equity effects will depend largely on what clinicians and patients do with generated text under time pressure and within imperfect workflows. The trust, verify, override heuristic provides a practical structure for postdeployment governance by translating broad principles into auditable routines, competency-based education, and pragmatic evaluation linked to action, including rollback.
More broadly, deploying AI is an intervention in human behavior. We encourage professional societies, accreditation bodies, and regulators to complement technical standards with expectations for postdeployment behavioral accountability, including observable verification practices, equity-based triggers, and learning-oriented incident systems. This accountability must also extend to AI developers and vendors, who share responsibility for limitation transparency, update disclosure, and structured adverse-event feedback from deploying organizations. Without these safeguards, GenAI may scale risk as readily as it scales efficiency.
Footnotes
Funding
The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.
Declaration of Conflicting Interests
The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.