Problem of Algorithmic Bias in Medicine: When Historical Healthcare Data Shapes Future Decisions

Artificial intelligence is becoming an increasingly important part of modern healthcare. Machine-learning systems can analyse medical images, estimate disease risk, identify patterns in electronic health records, support diagnosis, assist treatment decisions and help healthcare professionals process enormous quantities of clinical information. As AI-enabled medical technologies become more capable, the expectation is that they will make healthcare more accurate, efficient and personalised.

Yet artificial intelligence does not begin with a blank page.

Most medical AI systems learn from data generated by real healthcare systems. Those datasets contain information about patients, diagnoses, treatments, hospital visits, laboratory results, medical images and clinical outcomes. They also contain the influence of the healthcare systems that produced them. If historical healthcare has treated different populations differently, measured some patients more accurately than others, underrepresented certain communities or recorded clinical decisions according to unequal practices, those patterns can become part of the data used to train future algorithms.

This creates one of the most important challenges in medical AI: an algorithm can appear objective because it uses mathematics while still reproducing patterns of inequality embedded in the information from which it learned.

Research published in 2026 has reinforced this concern. A study in npj Digital Medicine found that fairness differences in healthcare machine-learning systems were driven more strongly by the dataset than by the choice of algorithm itself. The same algorithm could produce substantially different fairness outcomes when applied to different healthcare datasets.

The problem therefore extends beyond simply designing a better algorithm. It requires examining where healthcare data comes from, how it was collected, whose experiences it represents, how clinical labels were created and how an AI system behaves when introduced into a different population.

What Is Algorithmic Bias in Medicine?

Algorithmic bias occurs when an AI or machine-learning system produces systematically different or less accurate outcomes for certain groups of people. In healthcare, this can have particularly serious consequences because algorithmic outputs may influence diagnosis, risk assessment, treatment recommendations or access to medical services.

Bias does not necessarily mean that developers intentionally designed a system to discriminate. It can emerge indirectly from the data, measurement methods, clinical labels, sampling strategies, model design or environment in which the system is deployed.

For example, suppose a machine-learning model is trained primarily on data from one demographic group. The model may learn patterns that work extremely well for that population but perform less accurately for patients who differ in age, sex, ethnicity, socioeconomic background, geography or other clinically relevant characteristics.

The problem becomes even more complicated when the target variable itself reflects historical healthcare decisions. If previous clinical decisions were influenced by unequal access to care or inconsistent diagnostic practices, an algorithm trained to reproduce those decisions may learn the inequality rather than the underlying biological or clinical reality.

In this way, AI can transform historical patterns into automated predictions.

How Historical Healthcare Data Carries Bias Forward

Healthcare datasets are records of what happened in healthcare, not perfect representations of biological truth.

This distinction is fundamental.

If a patient did not receive a particular diagnostic test, the absence of that test does not necessarily mean the patient did not have the condition being investigated. If one population historically received fewer specialist referrals, a dataset may contain fewer diagnoses for that population even when the underlying disease prevalence was similar.

Similarly, medical records reflect the decisions made by clinicians working within particular healthcare systems, institutions and historical periods. Insurance structures, availability of specialists, geographic access, socioeconomic conditions and institutional practices can all influence what eventually appears in a medical record.

An algorithm may then interpret these patterns as meaningful predictors.

The result can create a feedback loop. Historical healthcare decisions influence the training data. The training data shapes an AI model. The model influences future healthcare decisions. Those new decisions become additional data, potentially reinforcing the original pattern.

The algorithm therefore does not simply predict the future. In some circumstances, it can help reproduce the past.

Bias Can Begin Before the Algorithm Is Built

One of the most important lessons from current research is that algorithmic bias cannot be addressed solely at the model-development stage.

Bias can enter much earlier.

It can begin when researchers decide which patients should be included in a dataset. It can emerge from differences in access to healthcare, differences in documentation quality, variations in diagnostic testing or inconsistencies in how clinical outcomes are defined.

A dataset may contain millions of records and still fail to represent the population that a healthcare system ultimately serves.

The 2026 npj Digital Medicine study provides an important illustration. Researchers evaluated multiple algorithms across several healthcare datasets and found that dataset characteristics explained substantially more variation in fairness outcomes than algorithm choice alone. Dataset identity accounted for 63.4% of the observed variability in gender accuracy gaps, compared with 9.7% attributable to algorithm choice.

This suggests that improving healthcare AI requires a data-centric approach. Developers cannot assume that selecting a sophisticated machine-learning architecture will automatically produce a fair system.

Representation Is More Than Counting Patients

A common response to algorithmic bias is to increase representation. This is important, but representation is more complicated than simply ensuring that every demographic group appears in a dataset.

A dataset can contain similar numbers of patients from different groups while still representing them unequally.

One group may have more complete medical records. Another may have fewer diagnostic tests. One population may be treated at highly specialised hospitals, while another is primarily represented through emergency departments. The same diagnosis may also be recorded differently across institutions.

Consequently, two groups can appear numerically balanced while having very different data quality.

Healthcare AI therefore needs to consider not only who is present in a dataset but also how those individuals were measured, diagnosed, treated and represented.

The World Health Organization has emphasised that health data used for AI should be ethically sourced, representative and appropriately governed. Strong health-data governance is considered essential for supporting reliable and equitable AI systems.

The Problem of Biased Clinical Labels

Machine-learning systems need something to learn from. In supervised learning, this usually means providing examples with labels such as disease status, treatment outcome or risk category.

But clinical labels are not always neutral.

A label such as “high risk” may be based on previous clinical decisions rather than an independently measured biological outcome. A diagnosis recorded in an electronic health record may depend on whether the patient had access to the appropriate specialist. A treatment label may reflect institutional preferences or historical practice patterns.

If these labels contain systematic differences between populations, the model can learn them.

This creates a difficult problem because developers may believe that they are training an algorithm on objective medical outcomes when they are actually training it on historical decisions.

The distinction between predicting what happened and determining what should happen becomes extremely important.

An AI system that accurately predicts historical healthcare decisions may not necessarily be making equitable or clinically appropriate recommendations.

Bias in Medical Images and Diagnostic Systems

Medical imaging is one of the most visible areas of healthcare AI. Algorithms can analyse X-rays, CT scans, MRI images, pathology slides, retinal photographs and other forms of medical imaging.

These systems can perform impressively when the images resemble those used during training. But imaging datasets can also contain demographic and institutional biases.

Image quality may vary between hospitals. Equipment manufacturers can differ. Imaging protocols may change. Certain populations may be more likely to receive particular types of imaging.

If an AI system learns correlations associated with the training environment rather than the underlying disease, its performance may decline when deployed elsewhere.

This is why validation across diverse populations and healthcare environments is essential. A model that performs well in a controlled research dataset cannot automatically be assumed to work equally well in every hospital.

The FDA now explicitly encourages transparency about training and testing data, known biases, underrepresented populations, confidence information and circumstances in which clinical inputs may differ from the data used during development.

When Efficiency Can Reinforce Inequality

AI is often introduced into healthcare with the goal of improving efficiency. Automated risk scoring, triage systems and decision-support tools can potentially help clinicians manage large patient populations.

However, efficiency is not automatically equivalent to fairness. If a system systematically assigns lower risk scores to a population that has historically received less intensive care, the technology may appear efficient while directing fewer resources toward patients who need them.

This is particularly concerning when an algorithm is used to allocate attention, referrals, follow-up appointments or specialised services. A model can therefore create inequality even when its developers never explicitly included demographic information as a decision variable.

This is because other variables can act as indirect proxies. Geographic location, healthcare utilisation, insurance-related information or previous treatment patterns may contain information correlated with socioeconomic circumstances or demographic characteristics. Removing an explicit demographic variable does not necessarily remove demographic bias.

Why Removing Race or Sex From a Model Is Not Always Enough

One intuitive approach to fairness is to remove sensitive attributes from the training dataset.

However, demographic characteristics can be indirectly encoded in other variables. Location, medical history, language, healthcare utilisation, environmental exposure and even certain physiological measurements may contain information correlated with demographic characteristics.

Removing a variable therefore does not necessarily prevent an algorithm from learning patterns associated with it.

In some clinical applications, demographic information may actually be medically relevant. Treating every demographic variable as inherently inappropriate can create its own problems.

The goal should instead be to understand why a variable influences predictions and whether that relationship reflects legitimate clinical differences, historical inequality or a combination of both. This is why fairness in medicine cannot be reduced to a simple rule such as “remove sensitive variables.”

Algorithmic Bias Can Change After Deployment

An AI system is not necessarily fixed once it has been validated. Healthcare environments change. Patient populations change. Clinical guidelines evolve. Diagnostic technologies improve. Diseases may change in prevalence. Physicians may alter their behaviour after interacting with an AI system.

These changes can create what is sometimes called performance drift. A model that performed fairly when it was developed may develop disparities when used in a different environment.

The 2026 research showing that fairness outcomes depend heavily on the dataset reinforces this concern. A model that appears equitable in one dataset cannot automatically be assumed to remain equitable in another clinical context.

This makes continuous monitoring important. AI governance therefore needs to extend beyond approval and initial deployment. Models need ongoing evaluation across patient populations and clinical settings.

The Difference Between Accuracy and Fairness

A major misconception is that a highly accurate AI system must also be fair. Accuracy is an overall measure. It can conceal differences between groups.

Imagine a system that performs extremely well for the majority population but considerably worse for a smaller group. Its overall accuracy could still appear impressive because the majority group dominates the dataset. Healthcare AI therefore needs subgroup-level evaluation.

Researchers may examine sensitivity, specificity, calibration, false-positive rates and false-negative rates across different populations. However, there is no single fairness metric that is appropriate for every clinical situation.

A metric that is useful for one application may be inappropriate for another. This is why fairness must be understood in relation to the clinical purpose of the system, the consequences of errors and the populations affected by those errors.

A 2026 systematic review of bias-mitigation strategies in healthcare AI found that researchers are using a range of approaches and fairness measures, but the effectiveness of different strategies varies by context.

How Can Healthcare AI Bias Be Reduced?

Reducing algorithmic bias begins with understanding the data before the model is trained.

Researchers need to examine who is represented, who is missing, how data was collected and whether clinical measurements were applied consistently. They also need to investigate whether the target outcome reflects an actual health outcome or merely a historical decision.

During model development, developers can test performance across relevant subgroups rather than relying only on aggregate accuracy.

Fairness auditing should continue during external validation and after deployment. If performance differs substantially across populations, the model may require modification, additional training data or restrictions on where and how it is used.

Technical interventions can also be useful. Researchers have explored methods such as reweighting datasets, improving sampling, fairness-aware learning, adversarial approaches, calibration strategies and other techniques. However, technical adjustments cannot compensate for every problem in the underlying data.

The most effective approach is therefore likely to combine better datasets, appropriate model design, subgroup evaluation, clinical oversight and continuous monitoring.

Transparency Is Becoming a Core Requirement

Transparency is increasingly important as AI systems become part of clinical decision-making.

Physicians need to know the circumstances under which an AI system was developed and validated. They need information about its intended use, limitations, known failure modes and populations that may be poorly represented.

Patients also have an interest in understanding when AI contributes to healthcare decisions.

The FDA’s current guidance for machine-learning-enabled medical devices specifically highlights transparency around training and testing data, limitations, underrepresented populations, known biases and ongoing performance monitoring.

Transparency does not guarantee fairness, but without transparency, identifying and correcting unfair performance becomes much more difficult.

The Role of Human Oversight

Human oversight remains essential when AI systems influence medical decisions.

A clinician can recognise contextual information that an algorithm may not have access to. A patient may describe circumstances that are missing from the electronic record. A physician may also recognise when an algorithm’s recommendation conflicts with established clinical knowledge or the patient’s individual situation.

However, human oversight is meaningful only if clinicians are able to question the AI.

If an AI system is treated as automatically authoritative, human involvement can become little more than a formal approval step.

Healthcare organisations therefore need to build workflows in which clinicians can understand, challenge and override AI recommendations when appropriate.

This is particularly important when the consequences of an incorrect prediction are serious.

Patients and Communities Should Be Part of AI Governance

Algorithmic fairness cannot be designed entirely inside technology companies or research laboratories.

Patients and communities affected by healthcare AI should have meaningful opportunities to participate in decisions about how these systems are developed and evaluated.

Different populations may identify risks that developers have overlooked. Patients may also have different views about acceptable trade-offs between accuracy, privacy, convenience and fairness.

The WHO’s 2026 work on responsible AI in health emphasises governance, data quality, validation, workforce capacity and participation by patients, communities and frontline professionals. It also warns that responsible progress should be measured by governance readiness rather than simply by deployment speed.

This represents an important shift. Responsible AI is not only a technical engineering problem. It is also a social and institutional responsibility.

The Future of Fairness in Medical AI

The future of healthcare AI will increasingly depend on moving from model-centric development toward system-level evaluation.

Instead of asking only whether an algorithm is accurate, healthcare organisations will need to ask where its data came from, which populations were represented, what assumptions shaped its labels, how performance varies between groups and what happens when the model is introduced into a new clinical environment.

This approach could lead to a more mature form of medical AI in which fairness is treated as an ongoing property rather than a one-time certification.

The rapid growth of AI-enabled medical devices makes this increasingly important. The FDA reported more than 1,600 AI-enabled medical devices authorised for marketing in the United States as of September 2026, demonstrating how quickly these technologies are moving into healthcare.

As deployment expands, the question will no longer be whether healthcare uses AI. The more important question will be whether healthcare systems can ensure that AI learns from history without automatically inheriting its inequalities.

Conclusion

Artificial intelligence has enormous potential to improve healthcare, but medical AI does not learn from an ideal world. It learns from healthcare systems that have their own histories, limitations, inequalities and measurement practices.

Historical healthcare data can therefore become a powerful source of algorithmic bias. When incomplete representation, unequal access, inconsistent diagnosis, biased clinical labels or institutional differences become part of training datasets, machine-learning systems may reproduce those patterns at scale.

The solution is not to abandon AI. Nor is it enough to choose a more sophisticated algorithm.

The evidence increasingly suggests that fairness begins with the data itself. The 2026 finding that dataset characteristics can have a greater influence on fairness than algorithm selection demonstrates why healthcare organisations need to examine data provenance, representation and context before deploying clinical AI.

Responsible medical AI requires representative data, transparent development, subgroup-level validation, continuous monitoring, meaningful clinical oversight and strong governance. It also requires recognising that accuracy and fairness are related but distinct objectives.

The most important principle may be simple: AI should learn from the past without blindly reproducing it.

If healthcare organisations can identify historical inequalities within their data and deliberately design systems to detect and address them, artificial intelligence could help reduce disparities rather than automate them. But achieving that future will require treating fairness not as an optional feature of medical technology, but as a fundamental part of how healthcare AI is designed, evaluated and governed.

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