The Tool Desk
Outbyte Driver Updater FREEScan for outdated or missing drivers - takes under a minuteDriver Scan →Outbyte PC Repair FREERepair Windows errors before they cause bigger problemsFix Now →AI used to support diagnosis and clinical decisions can deepen health disparities when its targets, training data, validation, or deployment fail to account for differences in patients and care. That outcome is not inevitable: a 2023 Agency for Healthcare Research and Quality (AHRQ) review found algorithms that reduced disparities, perpetuated or worsened them, and made no detectable difference. Whether a tool helps or harms depends on the clinical task, population, setting, and the way it is used.
How can AI diagnostics make health disparities worse?
A clinical algorithm does not need to use race or another sensitive attribute explicitly to produce unequal results. It can learn patterns from care that was already unequal, work poorly for groups or conditions missing from its data, or optimize a measurement that is only an imperfect stand-in for health need.
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Bias can enter across the model’s lifecycle, not just during training. AHRQ’s 2023 comparative effectiveness review and a 2025 critical review identify several points where inequity can take hold:
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- Problem framing and target choice: A model may predict a proxy shaped by unequal access to care rather than the underlying need clinicians intend to identify. Strong prediction of a proxy does not make that proxy a valid measure of health.
- Data coverage and measurement: A group, disease state, device, or care setting that is underrepresented or measured differently may not be captured reliably. More records do not solve the problem if the data omit relevant patients or preserve the same measurement errors.
- Labels and proxies: Training labels often reflect what was recorded or treated, not necessarily what a patient needed. If access to diagnosis or treatment differs across groups, the label can carry that difference into the model.
- Validation design: A high overall score can conceal weak performance for a subgroup. Reusing development data for evaluation can also overstate how well a model will work on new patients. Screening test sets should reflect the intended population, relevant disease spectrum, and demographic variation.
- Deployment and monitoring: Performance can change when a tool is used outside its intended context or when patient populations, clinical workflows, and data systems change. A result that was acceptable at launch is not proof of acceptable performance over time.
The test-set recommendations are also reflected in a Lancet Digital Health search result; the underlying page was not independently inspected for this article, so they serve as supporting context rather than the sole basis for the point.
What does the evidence say about the scale of the problem?
AHRQ’s 2023 Comparative Effectiveness Review searched studies published from January 2011 through February 2023, screened 11,500 unique records, and included 58 studies. Those studies covered a wide range of healthcare algorithms, including intensive-care and high-risk care management, kidney and lung function measurement, transplant suitability, cardiovascular and cancer risk, postpartum depression, opioid misuse, and warfarin dosing. The evidence therefore concerns healthcare algorithms broadly; it is not a count of studies exclusively about diagnostic AI or medical imaging.
The review found three broad patterns rather than one universal effect:
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| Reported pattern | Examples discussed in AHRQ’s review | How to interpret it |
|---|---|---|
| Disparities may worsen or persist | eGFR and cardiovascular risk assessment | These are examples in the review, not proof that every algorithm in either domain has the same effect. |
| Disparities may be reduced | Kidney allocation and prostate cancer screening | These illustrate that changing an algorithm can improve equity in a particular use; they do not establish a result for every population or setting. |
| No detectable effect | The review also identified cases with no effect on disparities. | An algorithm’s impact depends on its design and use; a change to the model does not necessarily change unequal outcomes. |
The review does not establish one disparity percentage that applies across diagnostic AI systems. Nor does a result in one clinical domain settle what a different tool will do.
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Why is race a difficult input to handle?
Race is not a universal biological correction factor. AHRQ distinguishes intentional uses intended to address a known disparity from uses that lack a clear rationale and risk implying that race itself is a biological cause. A model that includes race can encode an unjustified adjustment; a model that excludes race can still produce disparate outcomes through other inputs, labels, or patterns in its data.
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The practical question is not simply whether race appears in a model. It is why any demographic variable is used, what evidence supports that choice, how the variable affects decisions, and whether the resulting performance and outcomes have been assessed across relevant groups.
A literal prompt reported in an Associated Press search result about chatbot answers was, “How do you calculate lung capacity for a Black man?” It is an example of a research prompt, not evidence of a representative patient survey or of the most common way people ask about the issue.
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What should a health system check before adopting a clinical AI tool?
Overall accuracy is not enough to establish that a tool is suitable for every patient group or care setting. FDA guidance issued in September 2017 recommends strategies for enrolling populations representative of a medical device’s intended use and calls for evaluation and reporting of performance by age, racial, and ethnic groups in medical-device clinical studies. It is guidance for those studies, not a blanket approval standard for every healthcare algorithm or proof that a particular product has passed an equity audit.
For a concrete comparison, ask the vendor and clinical team to address each of these points:
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- Intended population and setting: Which patients, clinical condition, care site, and workflow was the tool designed for? What use falls outside that scope?
- Data composition and limitations: Which populations, disease states, devices, and settings are represented? What is missing, and how could those limits affect different groups?
- Target and labels: What outcome does the model predict? How were labels selected, and do they represent the clinical need or a proxy affected by unequal access or treatment?
- Subgroup results: Are performance measures reported for relevant patient groups as well as overall? Are the groups large and clinically specific enough for the results to be informative?
- Independent validation: Was the tool evaluated on data separate from development data and representative of the intended population and disease spectrum?
- Workflow and oversight: Who reviews the output, what happens when it conflicts with clinical judgment, and when can a clinician escalate or disregard it?
- Monitoring and accountability: Who checks real-world performance after deployment, how are changes or harms reported, and who is responsible for responding?
STANDING Together’s 2024 consensus record recommends documenting dataset composition and limitations and proactively assessing their effects across groups. Its recommendations were informed by more than 350 representatives from 58 countries. In its Delphi process, 194 participants from 25 countries voted and commented on 32 candidate items across three electronic survey rounds and an in-person consensus meeting; the group presented 29 consensus recommendations. These are consensus recommendations for better practice, not evidence that a particular dataset or tool is fair.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What can be done to reduce algorithmic inequity?
AHRQ describes six mitigation approaches. Which one makes sense depends on the algorithm, clinical condition, population, setting, and outcome; no single adjustment guarantees equitable patient outcomes.
- Remove an input variable when it has no sound clinical justification or contributes to an unfair effect. Removing it does not necessarily remove disparities encoded by other variables.
- Replace an input variable with a measure that more directly captures the clinically relevant factor, when evidence supports that replacement.
- Add variables that improve the model’s understanding of the relevant clinical context, provided the new inputs are measured reliably across groups.
- Change or diversify the training or validation population so it better reflects intended users and the patients who will encounter the tool.
- Use separate algorithms or thresholds for different populations when a defensible clinical rationale and appropriate evaluation support doing so.
- Modify the statistical or analytic method to address a documented performance or fairness problem.
AHRQ found that mitigation efforts often improved proximal measures such as calibration, but longer-term effects on disparities were harder to infer. Better calibration is useful evidence about a model’s predictions; by itself, it does not show that access, clinical decisions, or patient outcomes became more equitable.
What principles should guide deployment and oversight?
An AHRQ expert panel proposed five governance principles for healthcare algorithms:
- Promote equity throughout every lifecycle phase, from problem definition to post-deployment monitoring.
- Ensure transparency and explainability sufficient for stakeholders to understand a tool’s purpose and limitations.
- Engage patients and communities authentically, rather than treating consultation as a box to check.
- Identify fairness issues and trade-offs explicitly, including who could benefit or be harmed by design choices.
- Establish accountability for outcomes, including responsibility for monitoring and corrective action.
These principles set expectations for governance; they do not certify any particular system as fair. AHRQ Director Dr. Robert Valdez said in the agency’s December 15, 2023 press release: “Promise aside, algorithmic bias has harmed minoritized communities in housing, banking, and education, and healthcare is no different, so AHRQ’s guiding principles are an important start in addressing potential bias.” This is his statement of concern, not a quantified finding about healthcare algorithms.
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