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AI diagnostic tools can worsen health disparities when they learn from incomplete or biased data, optimize the wrong target, or are used beyond the setting in which they were tested. They can also help reduce disparities. The outcome depends on the specific tool, patients, clinical task, and safeguards—not on AI alone.
What the evidence says about AI and health disparities
There is no single effect that applies to all healthcare algorithms. The Agency for Healthcare Research and Quality (AHRQ) reviewed studies published from January 2011 through February 2023. It screened 11,500 unique records and included 58 studies spanning tasks such as kidney and lung function measurement, transplant suitability, cardiovascular and cancer risk, postpartum depression, opioid misuse, and warfarin dosing. The review found examples of algorithms that reduced disparities, perpetuated or exacerbated them, and had no effect.
That evidence is broader than diagnostic AI alone: it includes risk prediction and clinical decision support as well as measurement tasks. AHRQ cites eGFR and cardiovascular risk assessment among examples where algorithms may worsen disparities, and kidney allocation and prostate cancer screening among examples where changes may reduce them. These cases do not establish that every algorithm in those areas has the same effect.
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1Scan for outdated or missing drivers - takes under a minute2Clear out junk files and repair common Windows errors3Fix the driver behind crashes, sound loss and screen glitchesAHRQ Director Dr. Robert Valdez described the concern this way in a December 15, 2023, agency 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.” That is his statement of concern, not a quantified estimate of harm from healthcare AI.
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Where inequity can enter the lifecycle
Fairness is not determined only by a model’s code or by whether it includes demographic variables. Choices made before training, the conditions used to test a tool, and decisions made after deployment can all affect who benefits and who may be harmed.
Problem framing and targets
A model learns to predict the outcome it is given. If that outcome is a proxy for access to care, past clinical decisions, or another unequal process rather than the underlying health need, a model can reproduce the proxy’s distortions while appearing to perform its task. The AHRQ review and a 2025 critical review identify target-variable validity as a central concern.
Data coverage and measurement
Data may omit or poorly represent particular demographic groups, disease stages, devices, or care settings. Measurements and labels can also reflect differences in how people were assessed or treated. In either case, a model’s reported performance may not carry over to patients or circumstances that were missing from, or uncommon in, its development data. More data by itself does not resolve the problem if the data’s composition and limitations remain unknown.
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Validation and generalization
A result measured on the same data used to develop a model can overstate how well it will perform on new patients. A useful evaluation should reflect the intended population, relevant range of disease, and demographic variation. A test that does not include those features cannot establish that the tool works equally well across them.
Deployment and change over time
Even a model evaluated carefully for one clinical setting may be used in another, where patient mix, workflow, or available measurements differ. Performance can also change as populations and clinical practices evolve. AHRQ therefore calls for transparency, stakeholder awareness, and real-world evaluation before widespread implementation.
Why an overall accuracy score is not enough
A single headline performance figure can conceal important differences between patient groups. A model might perform well on average yet miss more cases, generate more false alarms, or produce less reliable estimates for a subgroup. Whether a difference matters depends on the clinical task and what happens after the model’s output is used.
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Evaluation should make clear who was represented, how performance varied across relevant groups, and whether the setting resembles the tool’s intended use. Demographic representation alone is not proof of fairness or safety: it must be paired with meaningful subgroup assessment and attention to limitations in the data and study design.
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Race is not a universal biological correction factor
Race should not be treated as a simple biological variable that automatically makes a prediction more accurate. AHRQ distinguishes intentional uses that aim to address a known disparity from uses without a clear rationale that can reinforce the mistaken idea that race itself is a biological mechanism. The right response depends on the clinical question and evidence, not on a universal rule to include or remove race.
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Removing race from a model’s inputs does not guarantee equitable results. Other variables can act as proxies, and inequalities in the data, targets, or clinical process can still shape predictions. Conversely, a carefully justified use of demographic information may be intended to identify or address a disparity. The reasoning and consequences of the choice need to be examined in context.
What mitigation can—and cannot—do
AHRQ describes six approaches used to mitigate algorithmic disparities. Each addresses a different potential source of inequity; none is a universal fix.
| Approach | What changes | What to assess |
|---|---|---|
| Remove an input | Exclude a variable from the model. | Whether other inputs still encode the same information, and how performance changes for relevant groups. |
| Replace an input | Use a different variable in place of the original. | Whether the replacement measures the intended clinical concept rather than introducing another proxy. |
| Add variables | Include information that may better capture clinical need or context. | Whether the additional data are reliable, available across groups, and appropriate for the intended use. |
| Change or diversify training or validation populations | Use a different or more varied patient population to develop or evaluate the model. | Whether the data reflect the intended patients, disease spectrum, and care settings. |
| Use separate algorithms or thresholds | Set different models or decision thresholds for different populations. | Whether the distinctions are clinically justified and whether their effects are monitored. |
| Modify the statistical or analytic method | Change how the model is built or how it makes predictions. | Whether the change improves the relevant clinical outcomes, not only a technical metric. |
AHRQ found that mitigation efforts often improved proximal outcomes such as calibration, which concerns how closely predicted risks match observed outcomes. Better calibration alone does not show that long-term disparities in patient outcomes have improved. That requires evidence about what happens in practice.
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How to assess a clinical AI tool
When comparing tools or reviewing a proposed deployment, ask for evidence on each of these points. A vendor’s overall accuracy figure or a claim of diverse data is not enough to answer them.
- Intended population and setting: Which patients, clinical task, and care environment was the tool designed for? Do they match the proposed use?
- Data and subgroup results: Who was included in development and evaluation, what limitations are documented, and how does performance vary across relevant groups?
- Target and labels: What exactly does the model predict, how were its labels chosen, and do they measure the clinical need or a proxy shaped by access or past decisions?
- Independent validation: Was the tool tested on data separate from development, with a disease spectrum and demographic variation relevant to its intended use?
- Workflow and oversight: Who sees the output, how does it affect decisions, what human review is required, and when can a clinician override or escalate it?
- Monitoring and accountability: Who checks performance after deployment, how are changes or harms reported, and who is responsible for responding?
STANDING Together’s 2024 consensus record offers a complementary data-quality safeguard: document a dataset’s composition and limitations, then proactively evaluate how those limitations may affect different groups. Its recommendations were informed by more than 350 representatives from 58 countries. In the 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. Consensus recommendations support responsible practice, but do not certify an individual dataset or tool as fair.
Governance needs to continue after launch
AHRQ’s expert panel sets out five principles for algorithm governance: promote equity throughout the lifecycle; ensure transparency and explainability; authentically engage patients and communities; identify fairness issues and trade-offs explicitly; and establish accountability for outcomes. These principles describe responsibilities, not a guarantee that any particular system will be fair.
In practice, a healthcare organization needs a way to notice when a model’s performance or use changes, assess effects on the people it serves, and act when the evidence points to a problem. Patients and clinical staff can help identify harms that a technical performance report might miss. The standard should be whether the tool supports sound care across its intended use—not whether it merely passed an evaluation once.
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