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Healthcare AI can shape who is diagnosed, treated, monitored, or offered limited resources. If its data, goals, testing, or deployment reflect unequal care, its recommendations may reinforce disparities. That is a credible patient-safety risk, not proof that every biased algorithm has caused a specific injury. Hospitals need to assess both how a model performs and what happens when people use it.
How can bias in a healthcare algorithm affect patient care?
An algorithm learns patterns from data and labels produced within healthcare systems. Those records can reflect differences in access, treatment, and documentation. A model may then reproduce or amplify those patterns, while its effects in practice depend on the clinicians, workflows, and patient population around it.
Problem formulation and prediction targets
Bias can begin with the question a system is built to answer. A target such as future healthcare spending, for example, is not automatically equivalent to a patient’s need for care. If the target reflects unequal access or prior treatment, a model that predicts it accurately could still direct attention or resources inequitably.
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Data selection and management
Groups that are missing, underrepresented, or poorly described in training data may receive less reliable predictions. Data quality can also vary across populations or care settings. These problems can affect what a model learns before it is ever tested.
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Development, validation, and clinical use
Overall performance can conceal differences between patient groups. Validation that does not reflect the intended clinical setting may miss those differences. Once deployed, a recommendation can influence decisions through clinician reliance, workflow design, and local practices; those factors can change who receives follow-up, treatment, or scarce resources.
Monitoring after deployment
Patient populations, data, and clinical workflows can change. A model that appeared acceptable during development may perform differently in a new setting or over time, so evaluation cannot end at launch.
What does “fairness” mean for a medical algorithm?
There is no single fairness number that answers every clinical question. A 2025 critical review by Coots and colleagues groups concerns in four areas. Each requires identifying the measure, the clinical outcome, and whose access or welfare could change.
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| Area of concern | Question to ask |
|---|---|
| Including or excluding race and ethnicity | How are these characteristics used, and what are the consequences of including or omitting them in this specific model? |
| Unequal decision rates | Do groups receive recommendations or resources at different rates, and are those differences clinically justified? |
| Unequal error rates | Does the model make different kinds or rates of errors across groups? |
| Bias in the prediction target | Does the outcome being predicted represent the clinical need or result that matters to patients? |
A difference between groups is not automatically unfair, and equal decision rates alone do not establish clinical fairness. The review examines seven prominent algorithms and cautions that strategies designed to improve one fairness measure may worsen outcomes across racial and ethnic groups. Fairness assessments should therefore report trade-offs and consequences rather than assume that one metric—or a race-blind approach—is sufficient.
What does the published evidence establish?
A 2025 review in npj Digital Medicine reports findings from earlier studies it examined. In one study sample of 48 healthcare AI studies, Kumar and colleagues’ 2023 assessment classified 50% as high risk of bias and 20% as low risk. In a separate sample of 555 published neuroimaging-based AI models for psychiatric diagnosis, the review reports that 86 studies (15.5%) included external validation, 97.5% included subjects only from high-income regions, and 83% were rated high risk of bias, citing Chen and colleagues.
These are findings about specific published study samples, as reported by the 2025 review—not estimates of all clinical AI or every tool in use. The review itself screened 233 potentially relevant articles and selected 94 for its final review. The evidence highlights concerns about representation and validation, but it does not establish the prevalence of patient harm across deployed systems or settle every specialty and intersectional disparity.
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How can a hospital evaluate an algorithm before using it?
Evaluation should connect technical performance to the intended decision and the people affected by it. Before adoption, a health system can ask:
- Subgroup performance: What are the model’s errors and relevant performance measures across patient groups? Are sample sizes and uncertainty reported?
- Decisions and access: Who is recommended for care, follow-up, or resources, and how do decision rates differ?
- Target validity: Does the label or proxy measure the clinical need or outcome the system is meant to address?
- Data quality and representation: Which populations and settings are represented in development and validation data, and where are gaps?
- Local validity: Has the tool been validated in the intended clinical environment, with its actual population and workflow?
- Human use: Can clinicians understand relevant limitations, exercise judgment, and raise concerns about recommendations?
- Patient and community input: Have affected people helped identify what outcomes, risks, and trade-offs matter?
- Accountability: Who reviews performance, investigates disparities, and has authority to change or stop use?
These checks should examine the model’s measured performance separately from downstream effects. A model can show acceptable aggregate results while influencing care in ways that warrant further scrutiny.
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What governance safeguards should continue after launch?
AHRQ and the National Institute on Minority Health and Health Disparities set out five guiding principles for addressing algorithm bias in racial and ethnic disparities in health and health care. The framework treats equity as a lifecycle responsibility, from problem formulation through monitoring and possible deimplementation.
- Promote equity at every lifecycle phase. Review problem formulation, data selection and management, development and validation, deployment and integration, and ongoing maintenance.
- Ensure transparency and explainability. Make a tool’s purpose, limits, and relevant basis for recommendations understandable to the people responsible for its use.
- Authentically engage patients and communities. Include affected people in identifying risks and defining meaningful outcomes.
- Identify fairness issues and trade-offs explicitly. Document which measures and outcomes are being optimized, what may worsen, and whose interests are affected.
- Establish accountability for equitable outcomes. Assign responsibility for monitoring, investigating concerns, taking corrective action, updating a tool, or ending its use.
NIST’s broader AI-bias guidance describes harmful effects that can occur regardless of intent and frames bias as a technology-and-process concern; it is not healthcare-specific. The World Health Organization’s 2021 health AI guidance says ethics and human rights should be central to design, deployment, and use, with accountability to healthcare workers, communities, and individuals affected by the technology.
The central patient-safety question is not only whether a model is accurate in a test. It is whether its target is appropriate, its performance is understood across the people it will affect, and the health system can detect and respond when its use contributes to inequitable care.
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