Validation is not enough: Longitudinal evidence of post-deployment fragility in clinical AI systems

These frameworks primarily focus on evidence generation at the point of evaluation and provide limited guidance on how performance degradation should be detected and managed during routine clinical operation, particularly when outcome labels are delayed, incomplete, or influenced by the model's own use.

“However, these frameworks primarily focus on evidence generation at the point of evaluation and provide limited guidance on how performance degradation should be detected and managed during routine clinical operation, particularly when outcome labels are delayed, incomplete, or influenced by the model’s own use [25–27].”

• cites [25] SPIRIT-AI and CONSORT-AI Working Group, 2020, Guidelines for clinical trial protocols for interventions involving artificial intelligence: The SPIRIT-AI Extension, BMJ sourcePartly supported

  Split into 2 atoms — 0 confirmed, 2 partially supported, 0 disputed, 0 misattributed, 0 unverifiable. Expand the claim to see per-atom verdicts.

  Experimental — accuracy not yet measured
• cites [26] SE Davis, 2024, Sustainable deployment of clinical prediction tools-a 360° approach to model maintenance, J Am Med Inform Assoc sourceDisputed

  Split into 2 atoms — 0 confirmed, 0 partially supported, 2 disputed, 0 misattributed, 0 unverifiable. Expand the claim to see per-atom verdicts.

  Checked by a reasoning model — may be wrong.

  Experimental — accuracy not yet measured
• cites [27] V Subasri, 2025, Detecting and remediating harmful data shifts for the responsible deployment of clinical AI models, JAMA Netw Open sourcePartly supported

  Split into 2 atoms — 0 confirmed, 2 partially supported, 0 disputed, 0 misattributed, 0 unverifiable. Expand the claim to see per-atom verdicts.

  Experimental — accuracy not yet measured

Care-process shifts, especially changes in test-ordering and documentation practices, can alter both feature availability and the meaning of missingness (informative missingness), which can affect deterioration-prediction performance even when underlying patient mix is relatively stable.

“Care-process shifts – especially changes in test-ordering and documentation practices – can alter both feature availability and the meaning of missingness (“informative missingness”), which can affect deterioration-prediction performance even when underlying patient mix is relatively stable [11,12].”

• cites [11] T Sim, 2025, Preserving informative presence: How missing data and imputation strategies affect the performance of an ai-based early warning score, J Clin Med sourcePartly supported

  Split into 2 atoms — 0 confirmed, 2 partially supported, 0 disputed, 0 misattributed, 0 unverifiable. Expand the claim to see per-atom verdicts.

  Experimental — accuracy not yet measured
• cites [12] J Singh, 2021, On missingness features in machine learning models for critical care: Observational study, JMIR Med Inform sourcePartly supported

  Split into 2 atoms — 0 confirmed, 2 partially supported, 0 disputed, 0 misattributed, 0 unverifiable. Expand the claim to see per-atom verdicts.

  Experimental — accuracy not yet measured

Clinical practice patterns learned from EHR data can vary substantially across years.

“Clinical practice patterns learned from EHR data can vary substantially across years [6].”

• cites [6] JH Chen, 2017, Decaying relevance of clinical data towards future decisions in data-driven inpatient clinical order sets, Int J Med Inform sourceSupported

Clinician interaction with decision support systems can recursively influence both inputs and outcomes, creating feedback loops that invalidate original performance assumptions.

“Complementary work on alert fatigue and automation bias shows that clinician interaction with decision support systems can recursively influence both inputs and outcomes, creating feedback loops that invalidate original performance assumptions [19,20].”

• cites [19] F Kücking, 2024, Automation bias in AI-decision support: Results from an empirical study, Stud Health Technol Inform sourceNot checked — no full text of the source
• cites [20] JS Ancker, 2017, Effects of workload, work complexity, and repeated alerts on alert fatigue in a clinical decision support system, BMC Med Inform Decis Mak sourcePartly supported

  The Discussion reports that alert acceptance fell as the number of reminders and repeated reminders increased, supporting the narrower point that interaction with decision support is associated with clinician responses. It does not show that those responses recursively change system inputs or create feedback loops that invalidate performance assumptions. The Discussion also says the study did not support desensitization from repeated exposure over time. (LLM, conf=0.85)

  Experimental — accuracy not yet measured

External evaluations of widely deployed proprietary systems, such as the Epic Sepsis Model, have reported poor discrimination and calibration in routine clinical use despite broad adoption.

“external evaluations of widely deployed proprietary systems, such as the Epic Sepsis Model, have reported poor discrimination and calibration in routine clinical use despite broad adoption [15,16].”

• cites [15] A Wong, 2021, External validation of a widely implemented proprietary sepsis prediction model in hospitalized patients, JAMA Intern Med sourceSupported
• cites [16] P Papareddy, 2025, Transforming sepsis management: AI-driven innovations in early detection and tailored therapies, Crit Care sourcePartly supported

  The Early Detection and Early Warning Systems section reports that the widely adopted Epic Sepsis Model had poor discrimination in independent validation (AUC about 0.63). The Foundational Issues section also reports that an external evaluation found it missed 67% of sepsis cases. The paper discusses poor calibration as a possible limitation of models generally, but does not report a finding of poor calibration for the Epic Sepsis Model specifically; page numbers are unavailable in our copy. (LLM, conf=0.88)

  Experimental — accuracy not yet measured

The performance of a customized SAPS-II ICU mortality model changed over time, illustrating how temporal changes in clinical case mix and care processes can affect the reliability of severity-adjustment models.

“Minne et al. showed that the performance of a customized SAPS-II ICU mortality model changed over time, illustrating how temporal changes in clinical case mix and care processes can affect the reliability of severity-adjustment models [10].”

• cites [10] L Minne, 2012, Effect of changes over time in the performance of a customized SAPS-II model on the quality of care assessment, Intensive Care Med sourceSupported

A pneumonia detection model learned site-specific and non-clinical confounders, resulting in large performance differences across hospitals despite strong internal validation.

“Zech et al. showed that a pneumonia detection model learned site-specific and non-clinical confounders, resulting in large performance differences across hospitals despite strong internal validation [14].”

• cites [14] JR Zech, 2018, Variable generalization performance of a deep learning model to detect pneumonia in chest radiographs: A cross-sectional study, PLoS Med sourceSupported

Predictive models may lose calibration and discrimination over time within the same institution, even when patient demographics and nominal case severity remain relatively stable.

“Longitudinal analyses have shown that predictive models may lose calibration and discrimination over time within the same institution, even when patient demographics and nominal case severity remain relatively stable [9].”

• cites [9] P Cabanillas Silva, 2024, Longitudinal model shifts of machine learning-based clinical risk prediction models: Evaluation study of multiple use cases across different hospitals, J Med Internet Res sourcePartly supported

  The Discussion (Principal Findings; page unavailable) reports calibration shifts for the delirium and sepsis models at the hospitals, and the Results (Data Source Analysis; page unavailable) reports a consistent proportion of women and men over time. But discrimination did not significantly decline: AUROCs remained stable across years at both hospitals. The copy also does not establish that case severity remained stable. (LLM, conf=0.88)

  Experimental — accuracy not yet measured

Dataset shift in medicine is the rule rather than the exception, driven by changes in care delivery, clinical incentives, and documentation rather than by random variation alone.

“Finlayson et al. argue that dataset shift in medicine is “the rule rather than the exception,” driven by changes in care delivery, clinical incentives, and documentation rather than by random variation alone [8].”

• cites [8] SG Finlayson, 2021, The clinician and dataset shift in artificial intelligence, New England Journal of Medicine sourcePartly supported

  In the 'TO THE EDITOR' section (page not available), the paper describes dataset shift as a major driver of clinical AI malfunction and lists common causes, including changes in behavior such as reimbursement incentives. It does not say that dataset shift is 'the rule rather than the exception' or specifically identify documentation changes as a cause. (LLM, conf=0.86)

  Experimental — accuracy not yet measured

Degradation may be linked to how data are produced within evolving workflows and information systems, not only to who the patients are.

“This supports a workflow-aware interpretation of post-deployment fragility: degradation may be linked to how data are produced within evolving workflows and information systems, not only to who the patients are [8,18,33,35].”

• cites [8] SG Finlayson, 2021, The clinician and dataset shift in artificial intelligence, New England Journal of Medicine sourceSupported
• cites [18] A Subbaswamy, 2020, From development to deployment: dataset shift, causality, and shift-stable models in health AI, Biostatistics sourceSupported
• cites [33] ES Andersen, 2024, Monitoring performance of clinical artificial intelligence in health care: A scoping review, JBI Evid Synth sourcePartly supported

  The Introduction says clinical AI performance may change during deployment when its operating environment changes. The Results’ input-monitoring section also discusses detecting shifts in model inputs. Neither passage specifically links degradation to evolving clinical workflows or information systems, so the source supports the broader point but not that proposed mechanism. (LLM, conf=0.79)

  Experimental — accuracy not yet measured
• cites [35] PL Day, 2026, Discovery of an artificial intelligence label feedback loop: How the success of a clinically implemented artificial intelligence algorithm has created an unforeseen challenge to algorithm retraining, Clin Chem sourceNot checked — no full text of the source

The validation-deployment gap arises within a single institution over time, not only through cross-site transportability failures.

“This extends critiques of retrospective validation by showing the validation–deployment gap arises within a single institution over time, not only through cross-site transportability failures [15,33].”

• cites [15] A Wong, 2021, External validation of a widely implemented proprietary sepsis prediction model in hospitalized patients, JAMA Intern Med sourcePartly supported

  The Introduction says Epic developed and validated the model using data from three health systems in 2013–2015; the Methods describe this study’s later evaluation at Michigan Medicine. The Discussion reports substantially worse performance than Epic’s earlier figures, supporting a validation–deployment gap, but the paper does not establish that the gap arose within one institution over time rather than across sites. (LLM, conf=0.85)

  Experimental — accuracy not yet measured
• cites [33] ES Andersen, 2024, Monitoring performance of clinical artificial intelligence in health care: A scoping review, JBI Evid Synth sourcePartly supported

  The Introduction (page unavailable) says clinical AI performance may change during deployment and that acceptable performance at validation does not guarantee sustained adequacy. This supports a gap developing over time, but our copy does not establish the more specific claim that it arises within a single institution rather than through cross-site differences. (LLM, conf=0.82)

  Experimental — accuracy not yet measured

Workflow-associated changes in data availability and timing were more strongly and consistently associated with post-deployment degradation than population-level indicators.

“Workflow-associated changes in data availability and timing were more strongly and consistently associated with degradation than population-level indicators.”

• AssumptionRests on an assumption: Operational telemetry indicators (input missingness, data latency, feature availability) validly capture workflow-induced drift and are not merely proxies for other unmeasured factors.

Outcome-based monitoring was delayed by label latency and documentation processes.

“whereas outcome-based monitoring was delayed by label latency and documentation processes.”

• AssumptionRests on an assumption: Outcome labels derived from routine clinical documentation are accurate and consistent enough to serve as ground truth for performance evaluation.

Label-independent operational signals, including input missingness and data latency, provided early indication of emerging fragility.

“Label-independent operational signals, including input missingness and data latency, provided early indication of emerging fragility”

• AssumptionRests on an assumption: Operational telemetry indicators (input missingness, data latency, feature availability) validly capture workflow-induced drift and are not merely proxies for other unmeasured factors.

Across all four clinically deployed AI systems, validation-era performance did not persist as a stable operational property after deployment.

“Across all systems, validation-era performance did not persist as a stable operational property after deployment.”

• AssumptionRests on an assumption: The validation-era performance metrics are a valid baseline for comparison, and the validation data are comparable to deployment data except for the factors studied.

AUROC changes during early deployment remained smaller in magnitude than calibration changes.

“In contrast, AUROC changes during early deployment remained smaller in magnitude, supporting the interpretation that calibration degradation emerged earlier than discrimination decline (Table 6).”

In Systems A and B, calibration slope attenuation and intercept drift emerged within months, preceding AUROC decline.

“In Systems A and B, calibration slope attenuation and intercept drift emerged within months, preceding AUROC decline.”

Workflow-associated telemetry signals, particularly missingness and latency, continued to precede or co-evolve with calibration degradation.

“Workflow-associated telemetry signals, particularly missingness and latency, continued to precede or co-evolve with calibration degradation.”

Input missingness and data latency preserved negative associations with calibration slope in all tested regression specifications.

“Input missingness and data latency preserved negative associations with calibration slope in all tested specifications.”

Leave-one-system-out analyses preserved the direction and ranking of associations between workflow-associated indicators and calibration degradation.

“Leave-one-system-out analyses preserved the direction and ranking of associations between workflow-associated indicators and calibration degradation.”

Feature availability change was negatively associated with calibration slope across Systems A-D, with standardized coefficients ranging from -0.19 to -0.37.

“| Feature availability change | -0.22 (-0.35 to -0.09) | -0.34 (-0.48 to -0.20) | -0.19 (-0.31 to -0.07) | -0.37 (-0.51 to -0.23) |”

• AssumptionRests on an assumption: Operational telemetry indicators (input missingness, data latency, feature availability) validly capture workflow-induced drift and are not merely proxies for other unmeasured factors.

Calibration changed earlier and more consistently than discrimination after deployment.

“Calibration changed earlier and more consistently than discrimination.”

• Also stated in: § 3.4. Longitudinal performance and telemetry trajectories · plain

  “Discrimination remained relatively stable early after deployment and declined later, whereas calibration deviated earlier and more consistently from validation benchmarks.”

• Also stated in: § 4.1. Principal findings and conceptual contribution · plain

  “Calibration degradation consistently preceded and exceeded discrimination loss after deployment: across multiple systems, slopes and intercepts drifted substantially while AUROC remained acceptable for extended periods.”

Rolling-window analyses confirmed sustained calibration divergence rather than transient instability.

“Rolling-window analyses confirmed sustained calibration divergence rather than transient instability (Fig 2).”

Input missingness and data latency were most strongly associated with declining calibration slope in Systems A and B.

“Input missingness and data latency were most strongly associated with declining calibration slope in Systems A and B.”

Across systems, post-deployment performance diverged from validation-era estimates despite unchanged model parameters, feature definitions, decision thresholds, and intended use.

“Across systems, post-deployment performance diverged from validation-era estimates despite unchanged model parameters, feature definitions, decision thresholds, and intended use.”

Calibration degradation was the earliest and most consistent post-deployment failure pattern.

“calibration degradation was the earliest and most consistent post-deployment failure pattern.”

Performance degradation unfolded gradually rather than as abrupt shifts.

“Performance degradation unfolded gradually rather than as abrupt shifts (Fig 1).”

Population demographics shift was not consistently associated with calibration slope across Systems A-D.

“| Population demographics shift | -0.05 (-0.12 to 0.03) | -0.04 (-0.11 to 0.04) | -0.03 (-0.10 to 0.05) | -0.06 (-0.14 to 0.02) |”

Across phase definitions, calibration changed more consistently than discrimination.

“Across phase definitions, calibration changed more consistently than discrimination.”

Calibration degradation remained detectable earlier than discrimination decline across alternative aggregation windows.

“Calibration degradation remained detectable earlier than discrimination decline across alternative aggregation windows.”

Across systems, degradation often appeared first as calibration failure without comparable loss of discrimination.

“Across systems, degradation often appeared first as calibration failure without comparable loss of discrimination.”

The direction and timing of the main findings were preserved across windowing specifications.

“The direction and timing of the main findings were preserved across windowing specifications.”

Feature availability change showed the strongest association in Systems B and D.

“Feature availability change showed the strongest association in Systems B and D.”

Confidence intervals widened during deployment, indicating increased performance heterogeneity under routine conditions.

“Confidence intervals widened during deployment, indicating increased performance heterogeneity under routine conditions.”

Input missingness was negatively associated with calibration slope across Systems A-D, with standardized coefficients ranging from -0.21 to -0.42.

“| Input missingness | -0.42 (-0.56 to -0.29) | -0.38 (-0.52 to -0.25) | -0.24 (-0.37 to -0.11) | -0.21 (-0.34 to -0.08) |”

• AssumptionRests on an assumption: Operational telemetry indicators (input missingness, data latency, feature availability) validly capture workflow-induced drift and are not merely proxies for other unmeasured factors.

Workflow-associated indicators showed larger and more consistent negative associations with calibration slope than population-related indicators.

“Workflow-associated indicators showed larger and more consistent negative associations with calibration slope than population-related indicators.”

• Also stated in: § 3.3. Quantitative associations between drift indicators and calibration degradation · boosted

  “Overall, workflow-associated indicators showed stronger and more consistent associations with post-deployment calibration degradation than population-related indicators.”

The temporal ordering between telemetry change and subsequent performance degradation was preserved in leave-one-system-out analyses.

“The temporal ordering between telemetry change and subsequent performance degradation was also preserved.”

Calibration drift consistently emerged as an early failure mode across the deployed systems.

“The consistent emergence of calibration drift as an early failure mode suggests that calibration stability should be treated as a primary safety objective”

• Also stated in: § 5. Conclusions · hedged

  “Across systems, degradation was progressive, often appeared first as calibration drift, and was more consistently associated with workflow-related telemetry signals such as missingness, latency, and feature availability than with broad population-level indicators.”

Workflow-aware signals were more consistently associated with degradation than broad population-level indicators.

“the results of this study indicate that workflow-aware signals were more consistently associated with degradation and may be more actionable for monitoring.”

• Also stated in: § 5. Conclusions · plain

  “Across systems, degradation was progressive, often appeared first as calibration drift, and was more consistently associated with workflow-related telemetry signals such as missingness, latency, and feature availability than with broad population-level indicators.”

Across systems, degradation was progressive.

“Across systems, degradation was progressive”

This longitudinal analysis of four deployed clinical AI systems shows that acceptable validation performance did not persist as a stable operational property after deployment.

“This longitudinal analysis of four deployed clinical AI systems shows that acceptable validation performance did not persist as a stable operational property after deployment.”

Post-deployment fragility can be a structural property of clinical AI systems embedded in evolving workflows.

“These findings suggest that post-deployment fragility can be a structural property of clinical AI systems embedded in evolving workflows.”

• AssumptionRests on an assumption: The four included AI systems are representative of clinically deployed AI systems in general, such that findings about post-deployment fragility generalize to other systems and settings.

Telemetry can serve as an early monitoring signal under conditions where complete outcome labels are delayed.

“These findings support the use of telemetry as an early monitoring signal under conditions where complete outcome labels are delayed.”

• AssumptionThe authors state this as an assumption: Outcome labels are assumed unavailable for real-time safety monitoring.

This study provides empirical, longitudinal evidence that post-deployment fragility can arise systematically across clinical AI systems operating under routine conditions.

“This study provides empirical, longitudinal evidence that post-deployment fragility can arise systematically across clinical AI systems operating under routine conditions.”

Preserved ranking does not ensure correct absolute risk estimation.

“Clinically, this failure mode is important because preserved ranking does not ensure correct absolute risk estimation.”

This creates a governance gap in which systems may continue operating despite telemetry evidence of emerging performance degradation.

“This creates a governance gap in which systems may continue operating despite telemetry evidence of emerging performance degradation.”

Acceptable validation performance may not provide durable assurance after deployment.

“This structure explains why acceptable validation performance may not provide durable assurance after deployment.”

Operational changes in how data were generated, documented, or made available may destabilize deployed models even when broad population characteristics do not change to a comparable extent.

“This pattern indicates that operational changes in how data were generated, documented, or made available may destabilize deployed models even when broad population characteristics do not change to a comparable extent.”

The observed longitudinal patterns were not artifacts of the selected temporal window.

“These analyses indicate that the observed longitudinal patterns were not artifacts of the selected temporal window.”

Silent miscalibration is a first-order safety risk, as it distorts absolute risk estimates and threshold-based actions without triggering discrimination-focused alarms.

“Silent miscalibration is therefore a first-order safety risk, as it distorts absolute risk estimates and threshold-based actions without triggering discrimination-focused alarms, highlighting a gap between commonly reported metrics and clinically meaningful safety properties.”

Increasing missingness reflected operational data availability and documentation timing rather than random feature loss.

“These patterns indicate that increasing missingness reflected operational data availability and documentation timing rather than random feature loss.”

Under outcome-based monitoring alone, degradation would be detected only after a delay.

“Under outcome-based monitoring alone, degradation would therefore be detected only after a delay.”

A second failure mode is workflow-associated degradation without comparable population-level change.

“A second failure mode was workflow-associated degradation without comparable population-level change.”

Discrimination metrics alone would have delayed detection of post-deployment degradation, particularly in Systems A and B, where calibration drift emerged before substantial AUROC decline.

“These findings indicate that discrimination metrics alone would have delayed detection of post-deployment degradation, particularly in Systems A and B, where calibration drift emerged before substantial AUROC decline.”

The telemetry-performance relationship was not driven by a single regression specification.

“These findings indicate that the telemetry–performance relationship was not driven by a single regression specification.”

Monitoring focused primarily on AUROC would delay detection of silent calibration failure.

“Because absolute risk estimates inform thresholds and escalation decisions, monitoring focused primarily on AUROC would delay detection of this failure mode.”

Operational signals were observable before or alongside performance degradation.

“Together, the telemetry and calibration trajectories indicate that operational signals were observable before or alongside performance degradation.”

Observed changes in model behavior reflected post-deployment operating conditions rather than intentional model modification.

“Therefore, observed changes in model behavior reflected post-deployment operating conditions rather than intentional model modification.”

The main finding - earlier and more consistent calibration degradation relative to discrimination decline - was not an artifact of the selected 6-month primary phase definition.

“These results indicate that the main finding – earlier and more consistent calibration degradation relative to discrimination decline – was not an artifact of the selected 6-month primary phase definition.”

A third failure mode is governance failure due to outcome-label latency.

“A third failure mode was governance failure due to outcome-label latency.”

• AssumptionThe authors state this as an assumption: Outcome labels are assumed unavailable for real-time safety monitoring.

Silent calibration failure occurs when models preserve ranking ability but systematically misestimate absolute risk.

“This represents silent calibration failure: the models preserved ranking ability but systematically misestimated absolute risk.”

Across all systems, model architecture, feature definitions, decision thresholds, and intended use remained unchanged during the observation period.

“Across all systems, model architecture, feature definitions, decision thresholds, and intended use remained unchanged during the observation period.”

Missingness contributed to calibration degradation by altering the information available to the model at inference time.

“This supports the interpretation that missingness contributed to calibration degradation by altering the information available to the model at inference time.”

Signals such as input missingness, data latency, feature availability, score-distribution change, and interaction logs are observable at or near inference time, before downstream clinical response is fully expressed.

“Signals such as input missingness, data latency, feature availability, score-distribution change, and interaction logs are observable at or near inference time, before downstream clinical response is fully expressed.”

The central challenge is not lack of data, but lack of conceptual and operational alignment between validation, monitoring, and governance.

“By positioning the present study alongside real deployed systems in Table 7, the analysis clarifies that the central challenge is not lack of data, but lack of conceptual and operational alignment between validation, monitoring, and governance.”

The study did not estimate system-specific time-to-loss-of-trust thresholds.

“Therefore, we did not estimate system-specific time-to-loss-of-trust thresholds.”

Real-world deployed clinical AI systems commonly rely on outcome labels that become available only after task-dependent delays ranging from hours or days to weeks or months.

“As summarized in Table 7, real-world deployed clinical AI systems commonly rely on outcome labels that become available only after task-dependent delays ranging from hours or days to weeks or months.”

The longitudinal degradation observed in this study, and mirrored by failures in real deployments, shows that the assumption that model performance remains stable unless explicitly updated does not hold.

“The longitudinal degradation observed in this study – and mirrored by failures documented in real deployments – shows that this assumption does not hold.”

• AssumptionThe authors state this as an assumption: Current practice often treats acceptable discrimination and calibration at validation as sufficient justification for deployment, with the implicit assumption that performance will remain stable unless the model is explicitly updated.

Once a deployed AI system influences clinical decisions, subsequent outcome labels may partly reflect model-guided intervention rather than the unmodified natural history of disease.

“Once a deployed AI system influences clinical decisions, subsequent outcome labels may partly reflect model-guided intervention rather than the unmodified natural history of disease.”

• Also stated in: § 4.4. Limitations and scope of interpretation · hedged

  “model-generated predictions may influence clinician behavior and subsequent outcome labels, creating label feedback that complicates outcome-based reassessment.”

The study is grounded in real-world deployment data.

“While this study is grounded in real-world deployment data, the empirical scope is limited to a finite set of clinical AI systems operating within specific institutional contexts.”

Many observed breakdowns can be reframed as governance failures - cases where degradation is detectable in principle but invisible under prevailing monitoring practices.

“It reframes many observed breakdowns as governance failures – cases where degradation is detectable in principle but invisible under prevailing monitoring practices.”

The empirical scope of the study is limited to a finite set of clinical AI systems operating within specific institutional contexts.

“the empirical scope is limited to a finite set of clinical AI systems operating within specific institutional contexts.”

Outcome labels rarely arrive fast enough for continuous safety assurance.

“Table 7 also underscores a common constraint: outcome labels rarely arrive fast enough for continuous safety assurance.”

This delay makes outcome-based monitoring insufficient as the sole mechanism for timely post-deployment safety surveillance.

“This delay makes outcome-based monitoring insufficient as the sole mechanism for timely post-deployment safety surveillance.”

Clinical AI fragility is an operational property of deployed systems under real governance constraints (e.g., delayed/biased labels).

“Together, these results frame clinical AI fragility as an operational property of deployed systems under real governance constraints (e.g., delayed/biased labels).”

Although telemetry such as missingness, latency, acknowledgement, and image quality is routinely available, it is rarely analyzed as a predictor of safety-relevant degradation.

“Although telemetry such as missingness, latency, acknowledgement, and image quality is routinely available, it is rarely analyzed as a predictor of safety-relevant degradation.”

Most studies assess performance at isolated time points, obscuring early-stage divergence from validation behavior.

“Most studies assess performance at isolated time points, obscuring early-stage divergence from validation behavior.”

The consistency of observed failure structures across heterogeneous systems supports the broader generalizability of the qualitative findings regarding timing, observability, and governance relevance of post-deployment degradation.

“the consistency of observed failure structures across heterogeneous systems supports the broader generalizability of the qualitative findings regarding timing, observability, and governance relevance of post-deployment degradation.”

Label-independent operational telemetry can provide earlier indication of degradation than outcome-based reassessment when labels are delayed.

“It demonstrates that label-independent operational telemetry can provide earlier indication of degradation than outcome-based reassessment when labels are delayed.”

• AssumptionThe authors state this as an assumption: Outcome labels are assumed unavailable for real-time safety monitoring.