While all types of organizations across sectors are using artificial intelligence (AI) tools, the benefits they gain from AI can vary based on what the organization is looking for. For healthcare organizations, AI can analyze large amounts of patient data and find trends that humans might miss, helping doctors diagnose diseases faster and predict outcomes better for their patients. But despite the advantages artificial intelligence can offer, AI bias in healthcare remains a challenge.
Understanding how AI bias in healthcare develops and how it affects providers’ decision-making is essential for anyone considering a career in healthcare. If you are interested in pursuing an entry-level job as a medical assistant, pharmacy technician, or medical office administrator, completing one of Fortis’s healthcare programs can help you get your start.
What Is AI Bias?
AI bias means unfair differences in how AI performs. One major cause of AI bias is using incomplete or inaccurate data to train AI systems. If the data that the AI learns from ignores certain populations of people or overlooks existing healthcare disparities among different groups, the same patterns of bias will appear in the information the AI system produces. The results are only as good as the information they are based on.
When AI bias in healthcare affects disease detection or treatment suggestions, patients’ health and even their lives can be at risk.
Bias can occur at any stage of an AI’s development, including when the data is collected, when the AI system is developed, and when the AI system is evaluated.
Data Bias
Data bias happens when the data used to train an algorithm — the set of instructions or rules used to train an AI system — does not reflect the entire population. If medical datasets have more records from one population group than others, the algorithm is mostly trained on data from this one group. The result is an inaccurate representation of patients from other groups.
Development and Expertise Bias
Bias can also come from the people who design AI systems. Developers, clinicians, and researchers all have their own viewpoints and perspectives, even unconscious ones. These assumptions can find their way into AI systems. An AI team might not have the range of experience and awareness needed to consider key aspects of certain patient groups or healthcare settings, which can create bias in the system.
Evidence and Research Bias
AI systems in healthcare rely on research and clinical evidence. But if certain populations are underrepresented in the studies AI systems rely on, they can inherit these limitations. This can affect the accuracy of predictions or recommendations for patients not included in the original research.
What Are Examples of AI Bias in Healthcare?
Many hospitals, research institutions, and public health organizations are using AI systems because of the clear advantages. Nvidia reports that active use of AI tools among healthcare organizations grew from 63% in 2024 to 70% in 2025.
However, AI systems may introduce or amplify bias. PLOS Digital Health reports that bias can be introduced and occur at any stage in the medical AI development pipeline, from when the data is annotated to when the research is published. Below are some examples of AI bias in healthcare.
Diagnostic Imaging Bias
If AI systems used in medical imaging are trained on datasets with limited demographic diversity, they may perform better for some groups than others. For example, if an AI tool to detect skin cancer relies on a dataset made up mostly of patients with lighter skin tones, it may be less accurate when checking patients with darker skin.
Predictive Health Risk Bias
An AI system used to estimate health risks for patients, such as the likelihood of a disease progressing or a chronic condition developing, analyzes large datasets from electronic health records (EHRs) and other sources to make predictions. If the data reflects patients with disparities in healthcare access or treatment, the system may unintentionally reinforce those patterns when making predictions.
For example, a 2026 Nature Health study found that patients who delayed or avoided care due to cost had less reliable health records, and predictive systems trained on that data performed worse for them as a result.
Workforce and Hiring Bias
AI bias in healthcare can also extend to administrative decisions. Hiring tools that screen resumes or evaluate applicants may rely on historical workforce data, which can fall short of full demographic representation. An AI system trained on that data may unintentionally reproduce those patterns when evaluating new candidates.
In 2023, the Equal Employment Opportunity Commission reached a settlement in its first case involving AI in hiring, after recruiting software rejected over 200 applicants solely because of their age.
How Can Healthcare Professionals Combat AI Bias?
Bias can happen at any stage of AI development, implementation, or use. So addressing it requires help from technical, clinical, and organizational groups. Teams may include data scientists, clinicians, public health experts, and other medical professionals. Patients and members of the community can also play an important part.
The Centers for Disease Control and Prevention (CDC) highlights five key components of effective AI in public health and medicine:
1. Inclusive data practices: Ensuring diverse datasets
2. Monitoring and evaluation: Continuously tracking AI system outputs
3. Ethical frameworks: Establishing ethical standards and promoting transparency
4. Public and professional education: Increasing public awareness and professional training
5. Community engagement: Implementing cultural competence and feedback mechanisms
Here are some steps healthcare professionals can follow to ensure the AI systems they use are designed and employed responsibly.
Improve Data Diversity
One of the most effective ways to reduce AI bias in healthcare is to make sure that training datasets include data from a wide range of populations. The more representative the dataset is, the better the chances the AI system will learn patterns that apply across large groups that include people with different backgrounds and experiences. To achieve this, data collection practices need to ensure participants from various demographic groups and geographic origins are represented.
Conduct Equity Audits
Regular evaluations of their AI systems help organizations track how the systems perform for different population groups. The key aim is to examine the accuracy of predictions and assess whether treatment recommendations vary significantly across different groups. When there are disparities, teams can make adjustments to improve the systems or the datasets.
Maintain Transparent Governance
Data governance and oversight are also key to reducing bias. Organizations can set up review processes and safeguards to track how their AI systems are trained, tested, and used. Proper governance can help identify unintended issues and ensure AI tools support fair and accurate decisions.
Prepare for a Healthcare Career
AI applications in healthcare range from analyzing medical images to identifying patterns in electronic health records. For healthcare organizations implementing these technologies, understanding AI bias, data governance, and algorithm evaluation is increasingly important.
If you are interested in preparing for an entry-level role in a rewarding field, enrolling in one of Fortis’s healthcare programs can help put you on the path. We offer the following programs: Medical Assisting, Medical Assisting With Basic X-ray, Medical Billing and Coding, Medical Office Administration, Pharmacy Technician, Sterile Processing Technician, and Lab Technician.
Find out how Fortis can help you start a career that matters.
Recommended Readings
A Day in the Life of a Medical Office Administrator
A Day in the Life of a Surgical Technologist
How Long Does It Take to Become a Medical Assistant?