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1Repair Windows errors before they cause bigger problems2Fix the driver behind crashes, sound loss and screen glitches3Clear out junk files and repair common Windows errorsDoctors should treat an AI treatment recommendation as decision support, not as a decision. Before acting on it, they need to confirm the tool is meant for this patient and clinical question, examine whether its validation evidence fits the setting, check the patient-specific inputs and limitations, and compare the result with their independent clinical assessment. If the output does not fit the case or its basis cannot be reviewed, the clinician should investigate or escalate rather than rely on a confident-looking answer.
Start by defining the decision and the tool’s intended use
First establish what decision needs to be made, then check whether the AI system is designed to support that particular decision. An output may look plausible and still be outside the tool’s intended scope.
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- Task: Does the tool address the decision in front of the clinician, or a different task such as screening, diagnosis, or risk estimation?
- Intended user and setting: Is it designed for the clinician using it and the type of care environment where it is being applied?
- Patient population: Does the patient fall within the population the tool is meant to cover?
- Inputs and output: Are the required inputs available, and is the output the kind of recommendation the tool is intended to provide?
The U.S. Food and Drug Administration’s Step 6: Is the Software Function Intended to Provide Clinical Decision Support? guidance identifies intended use and population, input information and data-quality requirements, an understandable description of the algorithm and validation, and relevant patient-specific knowns and unknowns as information that can support independent review. These are review criteria in U.S. guidance, not a complete worldwide regulatory test.
Examine what the validation evidence actually establishes
A validation result applies to a defined task, population, dataset, and setting. It does not establish that every recommendation is right for every patient, or that using the system improves treatment outcomes in every clinical workflow.
The World Health Organization’s 2023 Regulatory considerations on artificial intelligence for health recommends external validation using an independent dataset that represents the intended population and setting. It also recommends transparently documenting the dataset and performance measures, and grading clinical validation to the risk of the use. Results from development or historical data alone cannot show how well a tool will work in a different hospital, patient group, or workflow.
Doctors and health systems should therefore ask whether the evidence concerns the same clinical task and a sufficiently similar population and environment. They should also distinguish model performance on measured outcomes from evidence of clinical benefit. A result on one performance measure is not, by itself, proof that the tool leads to better decisions or outcomes.
Check that the output fits this patient
Before accepting a recommendation, check the information that went into it. Missing, outdated, inaccurate, or unusual inputs can make an otherwise valid tool unsuitable for the case. Then compare the output with the patient’s circumstances and the clinical picture available to the clinician.
- Confirm that required patient data are present and of the quality the tool expects.
- Check for patient characteristics that fall outside the intended population or are identified as limitations.
- Look for relevant patient-specific knowns and unknowns rather than assuming the tool has access to the full clinical context.
- Compare the recommendation with the clinician’s independent assessment and the facts of the case.
If the recommendation conflicts with the patient’s circumstances or the clinical assessment, that mismatch is a reason to investigate, seek another review, or use an appropriate escalation route—not a reason to assume the software is correct.
Match the strength of evidence to the consequences of error
The more consequential a wrong recommendation could be, the more important it is to require evidence and oversight suited to that risk. WHO recommends a risk-graded approach to clinical validation. It notes that 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 fit other situations. It does not set a universal trial requirement for every AI tool.
Clinical validation is not a single badge that makes a system trustworthy in every context. The appropriate evidence depends on the tool’s intended use, the potential harm from error, and how it will be used in practice.
Compare alternatives on the same criteria
When clinicians or health systems have more than one AI recommendation to assess, compare the evidence and fit on common dimensions. Headline accuracy figures alone do not reveal whether the tools address the same task or apply to the same patients.
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| What to compare | Questions to ask of each tool |
|---|---|
| Intended-use match | Does it cover this decision, intended user, patient group, and care setting? |
| Validation design | Was performance evaluated on an independent dataset representative of the intended population and setting? Is there clinical or prospective evidence appropriate to the risk? |
| Patient-level inputs and limitations | Are the required inputs available and suitable, and can the clinician see relevant patient-specific knowns and unknowns? |
| Evidence after deployment | Is performance monitored in the setting where the tool is used? Is there a process to review concerns and investigate changes? |
| Risk and oversight | Is the strength and type of evidence proportionate to the possible consequences of a wrong recommendation? |
Keep monitoring after deployment
A tool that performed acceptably during validation may become less reliable when the patient population, clinical setting, input data, workflow, or standard of care changes. This kind of change is often described as dataset shift. Local validation and ongoing monitoring can help identify a mismatch that was not apparent before deployment.
In their 2021 New England Journal of Medicine article, “The Clinician and Dataset Shift in Artificial Intelligence,” Finlayson and coauthors describe clinician vigilance and technical oversight as complementary. Frontline clinicians can flag outputs that appear systematically misaligned; governance teams can monitor measures such as accuracy and calibration and investigate concerns. WHO also recommends considering more intensive post-deployment monitoring for high-risk AI systems.
For a clinician, a practical response to a concerning output is to document the mismatch and use the institution’s reporting or escalation process. For a health system, that means having a clear route for concerns to reach people responsible for reviewing performance and deciding whether the tool needs further evaluation.
Understand the limits of a general validation checklist
No checklist can validate a particular product, treatment, or individual recommendation without evidence about that system and its use. The FDA guidance discussed here concerns U.S. clinical decision support criteria. WHO’s 2023 publication is a resource describing regulatory considerations, not a binding regulatory framework. Requirements and clinical practices depend on the specific tool, its intended use, specialty, jurisdiction, and local governance.
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