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A clinical model encounters a scan, note or patient profile that differs from the cases it was evaluated on. It can still produce a polished prediction—but should it? When a system’s uncertainty signal suggests a case may not be handled reliably, a safer design may be to withhold the answer and route the case for review. That only helps if the signal, the cases deferred and the follow-up workflow are all evaluated.
What does it mean for a model to be uncertain?
An uncertainty estimate is a signal about how reliable a model’s output may be; abstention is a decision to withhold that output or defer it. They are related, but they are not the same thing. A model can report uncertainty without refusing to answer, and an abstention rule can act on a signal without proving that the model would otherwise be wrong.
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Most importantly, uncertainty is not a correctness guarantee. A confident output can still be incorrect, while a low-confidence output may turn out to be right. The signal has value only if it is tested against outcomes in the intended clinical setting. A threshold copied from another task or population does not establish that the system is safe for a new one.
How should abstention be measured?
Selective prediction evaluates a model that can answer some cases and abstain on others. Reporting accuracy alone hides this choice: a system can appear more accurate on the cases it answers simply by deferring difficult cases. Report both the quality of answered predictions and the burden of deferral.
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| Measure | What it tells you | Question to ask |
|---|---|---|
| Performance among answered cases | How well the model performs on cases where it provides a prediction, using metrics appropriate to the clinical task. | Does performance remain acceptable at the coverage or referral rate the service can actually support? |
| Coverage | The share of cases for which the model provides an answer. | How much of the intended workload does the model handle? |
| Rejection or deferral rate | The share of cases the model does not answer; for a given set of cases, this is the complement of coverage. | How many cases need another route, and what kinds of cases are being deferred? |
Compare candidate uncertainty methods at matched coverage or referral rates, rather than comparing accuracy figures taken at different levels of abstention. Also examine which cases are rejected: the same overall deferral rate can conceal very different clinical burdens. These are useful evaluation dimensions, not a universal metric set prescribed for every device or task.
What does the medical-text study show—and what does it not?
A February 25, 2025 arXiv preprint by Vazhentsev and colleagues evaluates uncertainty methods across text-based mortality prediction, medical-code prediction, outpatient diagnosis classification and mental-health-related datasets. The authors report that results vary by task, which is a reason not to assume one method or threshold will transfer everywhere. Read the preprint.
For one outpatient dataset, the authors report that HUQ-RDE outperformed two comparison approaches on a rejection-curve metric and that rejecting the 40% most uncertain instances was associated with a 15% accuracy improvement. That is a result reported for that dataset and evaluation—not a forecast for a hospital deployment or a recommendation to reject 40% of cases.
The paper also reports that label-wise selective prediction can behave differently from instance-wise approaches, and that poorly calibrated class probabilities can undermine common threshold rules. A team choosing an approach therefore needs to test its behavior on its own task and data, including whether the uncertainty signal meaningfully separates cases that the model handles well from those it does not.
What should clinicians see when a model is uncertain?
A useful interface makes the system’s role and limits legible, rather than presenting a confidence number as a safety certificate. The FDA, Health Canada and MHRA transparency principles emphasize communicating relevant information about intended use, development, performance and, when available, logic to the people who need it. They also point to the importance of human-AI team performance and essential user information. See the FDA transparency principles.
For clinicians, that information can include known limitations and failure modes, relevant confidence intervals, gaps in data characterization, underrepresented populations, and input conditions that do not align with the development and validation data. Explain what an uncertainty indicator means in the product’s intended use; avoid implying that a particular score means the model knows it is wrong.
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Abstention also needs a clear destination. If the system defers a case, the interface and service workflow should identify who reviews it, what information they receive, and how the case proceeds if review is delayed or unavailable. Evaluate the human-AI team, not just the prediction component: deferrals create work, and the review route can affect both timeliness and clinical decisions.
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Test at the intended site and use
Before relying on abstention, evaluate the system on data and workflows that reflect the local intended use. Check performance among answered cases alongside coverage, the deferral rate and the type and severity of cases deferred. Review results across relevant patient groups and sites so an acceptable overall figure does not conceal weak performance or disproportionate deferral for a subgroup. Local acceptance testing is especially important when the deployed population or circumstances differ from development and evaluation data.
Plan for changes after deployment
Performance can change as data, patient populations and clinical practice change. FDA Commissioner Robert M. Califf emphasized ongoing monitoring in remarks to the Coalition for Health AI on March 5, 2024: “The ability of algorithm to provide accurate assessments will drift if left untended, often in unpredictable, and sometimes dangerous ways.” He also stressed using a valid sample of the population so estimates apply to the relevant population and clinical circumstances. Read Califf’s remarks.
Set a monitoring plan that checks operating characteristics over time, uses samples relevant to the population and circumstances of use, and specifies what happens when performance changes. That response may involve investigation, workflow changes or other controlled change management; the process should also support transparent reporting of successes and failures.
The World Health Organization’s 104-page framework, Generating Evidence for Artificial Intelligence Based Medical Devices, covers evidence generation from development through post-market surveillance and includes cervical cancer screening as a use case. Its publication record is dated November 17, 2021. See the WHO publication record.
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Abstention is useful when it improves the safety and reliability of the full clinical process—not merely the model’s score on the subset it chooses to answer. A health service needs evidence that answered cases perform adequately at a workable coverage level, that deferrals are understood and fairly assessed, and that a real follow-up path exists. If those conditions are not met, an uncertainty score alone does not make the system safer.
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