Doctors should treat an AI recommendation as evidence to assess—not as a treatment decision to accept automatically. Before relying on it, they need to check that the tool was designed for the clinical question and patient group, understand what evidence supports its output, assess whether the inputs fit this patient, and compare the result with their own clinical judgment. They also need a way to identify performance problems that emerge after the tool is put into use.
Start by checking what the AI tool is meant to do
A recommendation is relevant only if the system is intended to support the decision in front of the clinician. Before considering its answer, identify the tool’s intended user, patient population, clinical task, required inputs, and type of output. Check whether the current patient and care setting fall within those boundaries.
The U.S. Food and Drug Administration (FDA) describes information that can support a clinician’s independent review of clinical decision-support software, including intended use and population, input and data-quality requirements, an understandable description of the algorithm and its validation, and relevant patient-specific knowns and unknowns. These are FDA guidance criteria for the U.S. context, not a complete worldwide regulatory test.
If the tool is being used for a different patient group, setting, or decision than the one it was designed to support, a plausible-looking answer does not establish that it is suitable for that case.
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Examine the evidence behind the recommendation
Check whether validation matches the task
Ask what the system was evaluated on and whether that evaluation addresses the same clinical task, population, and setting. Performance evidence for one task or patient group does not, by itself, establish performance for another.
Look for independent, representative evaluation
The World Health Organization’s 2023 publication, Regulatory considerations on artificial intelligence for health, recommends external validation on an independent dataset that represents the intended population and setting. It also recommends transparent documentation of the dataset and performance measures. A result measured only on training or development data cannot establish how well the system will work in a different hospital, patient group, or workflow.
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Distinguish measured performance from clinical benefit
A validation result describes a defined task under defined conditions. It does not prove that every recommendation is right for an individual patient, or that using the tool improves patient outcomes. The strength and type of evidence should be proportionate to the consequences of an error; WHO recommends a risk-graded approach to clinical validation.
Check whether the recommendation fits this patient
Even a tool with relevant validation can produce an unsuitable recommendation if its inputs are incomplete, stale, inaccurate, or outside the range it was designed to handle. Review the data the system received and compare them with the patient’s actual circumstances.
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- Confirm that required inputs are present and sufficiently current and reliable.
- Check whether the patient’s characteristics fit the tool’s intended population.
- Look for important information the system may not have received, or limitations and unknowns that could affect its output.
- Compare the recommendation with the clinical facts and the clinician’s independent assessment.
A mismatch or unexplained result calls for investigation or escalation, not automatic acceptance because the software presents its answer confidently.
Match the evidence standard to the risk
The evidence needed depends in part on what could happen if the recommendation is wrong. WHO describes clinical validation requirements as risk-graded: randomized clinical trials may be appropriate for the highest-risk tools or when the highest standard of evidence is needed, while prospective validation in real-world deployment may suit other situations. This is not a universal requirement that every AI tool undergo a randomized trial.
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For a tool being considered for a consequential treatment decision, clinicians and health-system reviewers should be especially attentive to whether the evidence addresses the intended clinical use and whether the validation approach is strong enough for the potential harm of error.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Compare alternatives on the same criteria
When more than one AI recommendation is available, compare the evidence and limitations directly rather than relying on headline accuracy figures alone. The figures may refer to different tasks, populations, or evaluation conditions.
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| What to compare | Questions to ask |
|---|---|
| Intended-use match | Does the tool cover this clinical task, user, patient group, and care setting? |
| Validation design | Was the evaluation independent of development data and representative of the intended population and setting? Is the clinical evidence appropriate to the decision’s risk? |
| Inputs and limitations | Are the required inputs available and of suitable quality? Can the clinician see relevant patient-specific knowns and unknowns? |
| Post-deployment oversight | Is performance monitored, and is there a local process for review and reporting concerns? |
| Consequences of error | Is the strength and kind of evidence proportionate to the possible effects of a wrong recommendation? |
Keep checking after the tool is deployed
Validation before use is not a guarantee of continued performance. Changes in the patient population, clinical setting, input data, workflow, or standard of care can make a deployed system less reliable—a problem often described as dataset shift.
In a 2021 article in the New England Journal of Medicine, Finlayson and coauthors describe clinician vigilance and technical oversight as complementary. Clinicians can flag outputs that appear systematically misaligned with local practice or patient circumstances; governance teams can monitor performance, including accuracy and calibration, and investigate concerns. WHO recommends considering more intensive post-deployment monitoring for high-risk AI systems.
Local monitoring and a clear route for clinicians to report concerns help surface problems that may not have appeared in the original validation. They do not replace the initial review of intended use, evidence, and patient fit.
What this process can—and cannot—establish
These checks help clinicians judge whether a specific AI recommendation is relevant and supported enough to inform care. They do not validate every product or settle whether a particular treatment is right for an individual patient. Requirements and oversight also depend on the tool, its intended use, specialty, jurisdiction, and local governance. WHO’s 2023 publication is a resource describing regulatory considerations, not a binding regulatory framework.
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